[
    {
        "url": "http://arxiv.org/abs/2404.16807v1",
        "title": "Improving Diversity of Commonsense Generation by Large Language Models via In-Context Learning",
        "abstract": "Generative Commonsense Reasoning (GCR) requires a model to reason about a\nsituation using commonsense knowledge, while generating coherent sentences.\nAlthough the quality of the generated sentences is crucial, the diversity of\nthe generation is equally important because it reflects the model's ability to\nuse a range of commonsense knowledge facts. Large Language Models (LLMs) have\nshown proficiency in enhancing the generation quality across various tasks\nthrough in-context learning (ICL) using given examples without the need for any\nfine-tuning. However, the diversity aspect in LLM outputs has not been\nsystematically studied before. To address this, we propose a simple method that\ndiversifies the LLM generations, while preserving their quality. Experimental\nresults on three benchmark GCR datasets show that our method achieves an ideal\nbalance between the quality and diversity. Moreover, the sentences generated by\nour proposed method can be used as training data to improve diversity in\nexisting commonsense generators.",
        "authors": "Tianhui Zhang, Bei Peng, Danushka Bollegala",
        "published": "2024-04-25",
        "updated": "2024-04-25",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL"
        ],
        "label": "Original Paper",
        "paper_cat": "LLM Fairness",
        "gt": "Diverse Text Generation. A variety of methods have been proposed to enhance the diversity of NLG. Sampling-based decoding is an effective method to increase the generation diversity. Holtzman et al. (2019) proposed nucleus sampling to generate diverse content at the generation stage. Truncated sampling (Fan et al., 2018) prunes and then samples the tokens based on the probability distribution. Furthermore, Shen et al. (2019) proposed an MoE approach to diversify translation outputs. Moreover, incorporating external corpora in the MoE further promotes diversity, such as by using a knowledge graph (Yu et al., 2022; Hwang et al., 2023) or by a collection of retrieved sentences (Liu et al., 2023). Although LLMs have reported superior performance in numerous Natural Language Processing (NLP) tasks (Touvron et al., 2023; OpenAI, 2023b,a), to the best of our knowledge, diversifying their generations in commonsense reasoning with ICL has not been explored in prior work on GCR. In-Context Learning. Recent studies demonstrate that LLMs can exhibit robust few-shot performance on a variety of downstream tasks through ICL (Brown et al., 2020). ICL is a technique for instructing an LLM using one or more examples for a particular text generation task. The generated text is conditioned on both the input as well as the instruction prompt. Wang et al. (2023) show that in ICL, label words in the demonstration examples function as anchors, which aggregate semantic information to their word representations in the shallow (closer to the input) layers, while providing that information to the final predictions performed by the deeper (closer to the output) layers. In contrast to fine-tuning-based methods, ICL is computationally lightweight because it does not update the parameters of the LLM. Therefore, ICL is an attractive method when integrating task-specific knowledge to an LLM by simply changing the prompt and the few-shot examples (Dong et al., 2022).",
        "pre_questions": [],
        "main_content": "Introduction Commonsense reasoning is the ability to make logical deductions about concepts encountered in daily life, and is considered as a critical property of intelligent agents (Davis and Marcus, 2015). Concepts are mental representations of classes and are expressed using words in a language (Liu et al., 2023). Given the inputs, the GCR task requires a model to generate a high quality sentence that is grammatical and adheres to commonsense, evaluated by its similarity to a set of human-written reference sentences covering the same set of concepts (Lin et al., 2020). Often there exists multiple relationships between a given set of concepts, leading to alternative reasoning paths that take diverse view points. For example, given the four concepts dog, frisbee, throw and catch, different sentences can be generated as Dog; Catch; Frisbee; Throw A dog leaps to catch a thrown frisbee. The dog catches the frisbee when the boy throws it. A man throws away his dog's favourite frisbee expecting him to catch it in the air. A\u00a0dog catches\u00a0a\u00a0frisbee thrown\u00a0to it. A dog catches a frisbee thrown by its owner. A dog jumps in the air to catch a frisbee thrown by its owner. Figure 1: An example of diverse generated sentences sets in CommonGen (Lin et al., 2020) dataset. The generation shown at the bottom (in green ) are considered by human annotators to be more diverse than those at the top (in red ). shown in Figure 1. Although all sentences shown in Figure 1 are grammatical, the bottom set expresses diverse view points (e.g. from the dog\u2019s as well as the man\u2019s) compared to the set at the top. Apart from the generation quality, diversity is also an important factor in text generation because the low-diversity texts tend to be dull, repetitive or biased towards a particular view point (Tevet and Berant, 2021). Diversity is an important consideration in many Natural Language Generation (NLG) applications, such as story generation (Li et al., 2018), paraphrase generation (Gupta et al., 2018), and GCR (Yu et al., 2022; Liu et al., 2023). In GCR tasks, the input text often provides insufficient information to support diverse reasoning and generate multiple plausible outputs. Therefore, the diversity present in GCR task enables the exploration of different perspectives or all possible outcomes for a real-world situation. Existing methods promote diversity through special decoding strategies, such as nucleus sampling (Holtzman et al., 2019), or encoding interventions such as random noise injection (Gupta et al., 2018) or Mixture of Experts (MoE) approaches (Shen et al., 2019). We propose In-Context Diversification (ICD), a computationally-efficient and accurate method to improve the diversity in GCR, where the sentences are generated from a pre-trained LLM, and strikes arXiv:2404.16807v1  [cs.CL]  25 Apr 2024 a fine-balance between the output diversity and quality. ICD uses an ICL approach to increase the diversity of the sentences generated by an LLM, while maintaining the quality of the generation. ICD is a two-step process where it first lets an LLM to freely generate high-quality sentences that are grammatical, commonsense bearing and cover all the given input concepts. Next, ICD uses a userspecified diversity metric to evaluate the diversity of the generated sentences. If the diversity is low, ICD provides feedback to the LLM, instructing it to generate more diverse sentences considering the already generated sentences. Given that ICD is using LLMs to generate diverse sentences via ICL and without updating the parameters of the LLMs, an interesting and open question is whether an LLM can accurately judge the diversity of a given set of sentences, covering a common set of concepts. To answer this question, we conduct an experiment where we instruct GPT3.5-turbo to judge the diversity of the set of input sentences according to a five-scale grading system, and convert the predicted grades into binary judgements (i.e. diverse vs. non-diverse). We compare the LLM-assigned grades against those by a group of human annotators, and find a moderatelevel (Cohen\u2019s Kappa of 0.409) agreement between human vs. LLM judgements, demonstrating that LLMs can indeed be instructed to obtain diversity judgements for GCR tasks. We evaluate ICD on three GCR tasks/datasets: CommonGen (Lin et al., 2020), ComVE (Wang et al., 2020), and DimonGen (Liu et al., 2023). We find that our proposed ICD balances diversity and quality appropriately, improving their harmonic mean by at least 6% over that of a default baseline. Moreover, the sentences generated by ICD can be used as training data to improve diversity in a Seq2Seq model (Sutskever et al., 2014; Lewis et al., 2020), producing results that are comparable to the models that are trained on knowledge graphs or human-written text corpora (Liu et al., 2021; Fan et al., 2020; Li et al., 2021). We consider the problem of generating a set of diverse sentences that express commonsense reasoning, either by covering a set of given concepts (in CommonGen and DimonGen) or by providing an explanation for a given counterfactual statement (in ComVE). Formally, given a sequence (a set of concepts or a statement) X = {x1, . . . , xm}, the goal of GCR is to generate a set of grammatically correct and commonsense bearing sentences Y = {y1, . . . , yn}, where yi is the i-th output generated by the model with probability p(yi|X). Moreover, we require that the generated sentences {y1, . . . , yn} to be lexically as well as semantically diverse. Default Examples: Given several key words: [SRC],  generate one coherent sentences using  background commonsense knowledge:  [TGT]  Test instruction: Given several key words: [INPUT],  generate one coherent sentence using  background commonsense knowledge:  [OUTPUT] Diversified Examples: Given several key words: [SRC], generate one coherent sentence using  background commonsense knowledge:  [TGT]  Test instruction: Step1: Given several key words: [INPUT],  generate [N] different and coherent  sentences using background commonsense  knowledge: [PRV] (If the diversity of [PRV] is low) Step2: You have generated the following  sentences: [PRV], try to provide other  reasonable sentences: [OUTPUT] (a) (b) Figure 2: An example of default and diversified prompts is shown for an instance selected from the CommonGen dataset. Here, the default prompt shown in Figure 2a is taken from Li et al. (2023). Few-shot examples are included in each prompt where [SRC] denotes the set of input concepts and [TGT] the corresponding sentences in CommonGen. For a given set of [INPUT] concepts, the LLM is then required to generate sentences at the slot [OUTPUT]. As shown in Figure 2b, ICD uses the diversified prompt, which operates in two steps. Step 1 generates a set of [N] sentences, [PRV]. We check for the diversity among the sentences in [PRV], and if it is low, we use the prompt in Step 2 to generate the final set of sentences. 3.1 Sentence Generation To explain our proposed ICD, let us consider GCR on CommonGen, where we must generate a set of sentences Y, such that each sentence contains all of the input concepts X as shown in Figure 2a. Given an LLM, we can design a prompt that contains a task-specific instruction and one or more examples containing the input concepts (denoted by [SRC] in Figure 2) and the corresponding human-written sentences containing all given input concepts (denoted by [TGT]) to instruct the LLM to generate output sentences Y (denoted by [OUTPUT]) for a given set of input concepts X (denoted by [INPUT]). We refer to a prompt of this nature as a default prompt, and the corresponding set of generated sentences by Sdef. Note that the default prompt does not necessarily guarantee that the generated set of sentences will be diverse and an LLM could return sentences that are highly similar to each other. To address this issue, we propose a diversified prompt as shown in Figure 2b. Specifically, the diversified prompt operates in two steps. In Step 1, we require that the LLM generate N sentences that are different, in addition to being coherent and commonsense bearing. Next, we use a suitable diversity metric to evaluate the level of diversity among the generated set of sentences. If the diversity of the generated senAlgorithm 1 In-Context Diversification (ICD) Input: Generated sets of sentences Sdef and Sdiv, respectively from default and diversified prompts, the number of desired output sentences N, and a diversity metric f. Output: Output set of sentences S\u2217 S\u2217\u2190\u2205 \u03b1 \u21900 for S \u2208(Sdef \u222aSdiv) do if (|S| == N) \u2227(f(S) \u2265\u03b1) then \u03b1 \u2190f(S) S\u2217\u2190S end if end for return S\u2217 tences is low, in Step 2, we show those sentences to the LLM and instruct it to generate sentences that are different to those. As the criteria for triggering Step 2, we check whether the exact same sentence has been generated multiple times by the LLM during Step 1. The final set of generated sentences is denoted by Sdiv. 3.2 Diversity-based Sampling Because of the limited availability of humanwritten reference sentences for evaluating GCR models, there exists a trade-off between quality vs. diversity when generating sentences for GCR tasks.1 Simply maximising for diversity often leads to generations that do not cover the input concepts in a natural way. For example, a randomly selected set of sentences would be highly diverse, yet unlikely to capture the input concept sets. On the other hand, if we force an LLM to generate sentences that contain all of the input concepts, it might find difficult to generate semantically diverse sentences and resort to trivial lexical or syntactic diversity tricks such as morphological inflections or word-order permutations. To address this issue, we propose a diversitybased sampling method shown in Algorithm 1. Consider that the default prompt provides a set Sdef of sentences that have not been optimised for diversity (likely to have a higher quality), while on the other hand the diversified prompt provides a set Sdiv of sentences that are further refined for diversity (likely to have a higher diversity). We wish to find a set of sentences that simultaneously satisfies the following criteria: (a) must contain exactly N sentences, as specified by the user, and (b) must have a high diversity score, measured using a user-specified diversity metric f(\u2208R\u22650). We formalise this as a subset search problem, where 1This trade-off is further empirically verified in \u00a7 5.1. we compute the union Sdef \u222aSdiv and search for the subset S\u2217that jointly satisfies those criteria following the procedure detailed in Algorithm 1. Although the total number of subsets of size N is \u0000|Sdef\u222aSdiv| N \u0001 , it is sufficiently small for the values of N in our GCR tasks, which makes this subset search fast in practice. 4 Experimental Settings 4.1 Tasks and Datasets We evaluate ICD on three GCR tasks as follows. Constrained Commonsense Reasoning: In CommonGen (Lin et al., 2020) benchmark, a model is required to generate a sentence covering a given set of concepts such that background commonsense knowledge associated with the input concepts is reflected. This dataset contains 35K distinct concept sets (train = 32651, dev = 993, and test = 1497) with corresponding human written sentences (train = 67389, dev = 4018, and test = 6042). Each instance contains on average 3-5 input concepts. Commonsense Explanation Reasoning: ComVE (Wang et al., 2020) is part of the SemEval 2020 commonsense validation task, where for a given counterfactual statement, a model is required to generate an explanation providing a reason describing why the statement is nonsensical. This dataset contains 10K (train = 8532, dev = 476, and test = 992) examples, where each example contains three reference outputs. Diversified GCR: DimonGen (Liu et al., 2023) involves generating diverse sentences that describe the relationships between two given concepts. It is a challenging task because it requires generating reasonable scenarios for a given pair of concepts without any context. This dataset contains 17109 instances (train = 15263, dev = 665, test = 1181), where each instance has 3-5 references. 4.2 Evaluation Metrics We measure both the quality and diversity of the sentences generated by models using the metrics described next. 4.2.1 Quality Metrics We compare a generated sentence by a model against a set of human-written references to evaluate the quality of the generation using several metrics: BLEU (Papineni et al., 2002) measures n-gram precision against human reference texts, SPICE (Anderson et al., 2016) measures the semantic propositional overlap between two sentences, and BERTScore (Zhang et al., 2020) uses contextualised word embeddings to measure the semantic similarity between tokens in two sentences. In alignment with prior works (Yu et al., 2022; Liu et al., 2023; Hwang et al., 2023), when multiple candidate sentences are generated for a test case, we select the highest-scoring candidate for evaluating quality. 4.2.2 Diversity Metrics Pairwise Diversity: We use self-BLEU (Zhu et al., 2018) to measure n-gram overlap among sentences within each generated set. The metric computes the average sentence-level similarity between all pairwise combinations of the generations in the generation set. Note that unlike BLEU, self-BLEU does not require human generated references for measuring diversity. We use self-BLEU3/4 (corresponding to n = 3 and 4) in our experiment. Lower self-BLEU scores indicate higher lexical diversity. Corpus Diversity: To measure the variety within our generated text corpus, we employ Distinctk (Li et al., 2016), which calculates the ratio of unique k-grams to the total number of k-grams. This metric is particularly useful for adjusting the bias of LLMs toward generating longer sequences, ensuring that diversity is not artificially inflated by the sentence length. Additionally, we use Entropyk to evaluate the distributional uniformity of kgram occurrences, considering word frequencies for a more nuanced view of diversity. Higher Distinct-k and Entropy-k scores indicate higher diversity. Semantic Diversity: All previously described diversity metrics are limited to evaluating lexical diversity. To measure diversity at a semantic level, we propose self-cosSim, which is the average pairwise cosine similarity between generated sentences, computed using sentence embeddings obtained from SimCSE (Gao et al., 2021). Likewise, we define the self-BERTScore as a diversity metric that averages the BERTScores for all generated sentence pairs. Lower self-cosSim and self-BERTScore values indicate higher semantic diversity. 4.2.3 Combined Metrics We would prefer GCR models that have both high quality and high diversity. To incoporate both aspects into a single metric, we compute the Harmonic Mean between (a) the self-BLEU-4 as the diversity metric, and (b) BERTScore as the quality metric. As discussed in \u00a7 3.2, there exists a tradeoff between quality and diversity in GCR. Therefore, the harmonic mean is suitable when averaging quality and diversity scores.2 Alihosseini et al. (2019) proposed Fr\u00b4 echet BERT Distance (FBD) as a joint metric for simultaneously measuring both the quality and diversity of NLG. FBD is inspired by the Fr\u00b4 echet Inception Distance (FID), proposed by Heusel et al. (2017), for measuring the quality of image generation. Specifically, FBD computes the pooler output3 of a sentence as its embedding (Devlin et al., 2019) and represents a set of sentences using the mean vector and the covariance matrix computed from their sentence embeddings. Next, Wasserstein-2 distance is computed between the set of reference sentences and the set of generated sentences, which captures both the distance between the means as well as variance in the distributions. Lower FBD scores indicate high combined performance. 4.3 Implementation Details We use GPT3.5-turbo and Vicuna-13b-v1.54 as LLMs with temperature set to 1.0 in our experiments. By using two LLMs with significantly differing number of parameters and by including, Vicuna, an open source LLM, we plan to improve the reliability and reproducibility of our results. Max response length is set to 25 tokens. The inference times for CommonGen, ComVE and DimonGen datasets are respectively 5-6, 2-3 and 1-2 hours. The cost of running ICD with GPT3.5-turbo are ca. $6, $4 and $4 respectively for CommonGen, ComVE and DimonGen datasets. On the other hand, the costs of fine-tuning on GPT3.5-turbo are much higher at $58.8 for CommonGen, $24.7 for ComVE and $32.0 for DimonGen. Moreover, fine-tuning with LoRA (Hu et al., 2022) with rank of 8 and alpha of 16 on Vicuna takes ca. 34 hours. We use BART-large5 for MoE-based models. We use the GPT3.5-turbo to generate sentences for the CommonGen train/dev/test sets using the de2We use self-BLEU-4 for diversity and BERTScore for quality in Harmonic Mean due to their reliability shown in preliminary evaluations. Other metric pairs are in Appendix D. 3The last layer\u2019s hidden-state of the first token of the sequence is further processed by a Linear layer and a Tanh activation function. 4https://huggingface.co/lmsys/vicuna-13b-v1.5 5https://huggingface.co/facebook/bart-large fault, diversified and for ICD. For model training, we use the Adam optimiser (Kingma and Ba, 2015) with a batch size of 64, a learning rate of 3e-5 and a beam size of 5. All of the MoE-based models are trained for 20 epochs and required to generate k = 3 sentences. All experiments, except with GPT3.5-turbo, are conducted on a single RTX A6000 GPU. 5 Results and Discussion 5.1 Commonsense Generation We compare the commonsense generations made by ICD against those using the default and diversified prompts. For this purpose, we use GPT3.5-turbo as the LLM and use the same 10 few-shot examples in all prompts for ICL. Further templates of the default and diversified prompts used for each task are given in Appendix E. To assess the impact of ICL, we compare against finetune method, wherein GPT3.5-turbo is fine-tuned on the entire training set in each dataset. Specifically, we use multiple human-written sentences, available in the training data for the three datasets to separately fine-tune the models for each task. It is noteworthy that the fine-tune method uses a substantially larger dataset for training (e.g., 67,389 sentences from CommonGen) compared to the 10 examples used by the ICL-based approaches. We use self-BLEU-3 as the diversity metric f in Algorithm 1 for ICD in this evaluation. The outcomes, presented in Table 1, highlight the diversity and quality metrics of these methods across the CommonGen, ConVE, and DimonGen datasets. Additionally, a human baseline is introduced to evaluate the diversity of sentences written by humans, where we pair-wise compare the human-written sentences for each input in the instances in the benchmark datasets using diversity metrics. Note that however, the human baseline must not be considered as an upper-bound for diversity because there are only a smaller number of human-written sentences per instance in the benchmark datasets. From Table 1, we see that fine-tune generates sentences that have high semantic and corpus diversity, and outperforms the human baseline. However, recall that fine-tune requires a much larger training set and is computationally costly compared to all ICL-based methods. Moreover, we see that ICD can strike a good balance between quality and diversity in the sentences generated. Among the ICL-based methods, ICD achieves the best diSemantic Diversity \u21d3 Corpus Diversity \u21d1 Pairwise Diversity \u21d3 Quality \u21d1 Combined self-cosSim self-BERTScore Entropy-4 Distinct-4 self-BLEU-3 self-BLEU-4 BLEU-3 BLEU-4 SPICE BERTScore Harmonic \u21d1 FBD \u21d3 CommonGen Human 67.3 60.6 10.9 91.0 25.4 17.6 Fine-tune 64.7 55.9 11.4 91.1 26.9 17.9 41.2 32.1 30.3 64.2 72.1 51.9 default 93.3 88.7 10.2 53.7 77.2 72.4 50.8 40.9 30.1 70.4 39.6 60.2 diversified 85.2 69.8 11.0 83.7 44.4 34.9 44.3 34.6 28.5 65.0 65.4 53.9 ICD 83.5 66.2 11.0 88.5 31.0 21.0 47.4 37.7 29.1 67.4 72.7 51.8 ComVE Human 62.7 47.0 9.6 96.1 12.4 8.1 Fine-tune 59.8 42.6 9.8 95.2 13.4 10.3 27.4 19.4 33.1 53.7 67.2 47.6 default 83.9 73.5 9.6 74.3 50.8 45.2 27.5 19.7 36.2 55.1 54.9 50.9 diversified 76.1 56.5 9.7 88.0 23.2 16.5 30.5 21.8 35.8 56.5 67.4 47.9 ICD 72.5 51.1 9.8 90.1 13.7 8.7 29.0 20.8 36.1 55.5 69.0 48.7 DimonGen Human 56.8 47.0 10.1 85.6 14.7 8.7 Fine-tune 43.4 33 10.4 98.7 6.8 3.4 17.7 10.7 15.5 42 58.5 51.6 default 75.7 71.3 10 83.2 43.4 37.3 15.9 9.5 16.4 44.5 52.1 68.2 diversified 57.1 46.9 10.5 95.9 11.2 6.5 11.4 6.4 15.2 39.9 55.9 69.0 ICD 56.7 45.7 10.4 96.3 6.5 3.5 13.2 7.6 15.4 41.7 58.2 68.0 Table 1: Diversity and quality scores on CommonGen, ComVE and DimonGen with GPT3.5-turbo LLM. Best results on each task for each metric are shown in italics, while the best performing ICL results are shown in bold. versity scores on all diversity metrics in all three datasets. It also exhibits higher diversity compared against the human-written references. Moreover, ICD outperforms default and diversified according to the Combined metrics. ICD also achieves a Harmonic Mean comparable to that of the fine-tune baseline. Although default reports the best quality scores, it has low diversity, and is consistently outperformed by diversified and ICD on diversity metrics. On the other hand, diversified generally scores lower on the quality metrics. Compared to default and diversified, ICD enhances generation diversity while maintaining a satisfactory level of quality. ICD is also more stable to the sampling method such as temperature than fine-tune, as shown in Appendix B. Note that fine-tune is not an ICL setting (the focus of this paper) and is included only as a baseline to demonstrate the level of performance that can be achieved by finetuning on a much larger dataset. Despite this, ICD outperforms fine-tune on the Pairwise Diversity in all three datasets, and Combined metrics in the CommonGen dataset. As an open source alternative LLM to GPT3.5-turbo, we repeat this evaluation with Vicuna-13b (Zheng et al., 2023) in Table 2. The same 10 few-shot examples as used with GPT3.5-turbo are used in this experiment for the ICL-based methods. Full table on three datasets are shown in Appendix C. Table 2 reconfirms ICD\u2019s ability to balance both quality and diversity according to the Combined metrics (i.e. Harmonic Mean and FBD) on this dataset. Interestingly, we see that Method SCS \u21d3 SBS \u21d3 E-4\u21d1 D-4\u21d1 SB-3\u21d3 BLEU-3\u21d1 SPICE\u21d1 HM \u21d1 FBD \u21d3 Fine-tune 59.6 49.9 11.4 93.3 22.8 35.8 27.6 69.9 52.4 Default 82.2 73.8 10.9 74.9 52.9 44.6 29.1 60.2 56.2 Diversified 59.1 53.3 11.3 91.3 23.6 32.6 24.3 68.6 53.2 ICD 59.3 49.8 11.3 93.7 11.3 34.2 25.5 73.4 51.0 Table 2: GCR on CommonGen using Vicuna-13b. ICD uses self-BLEU-3. Here, SCS: self-CosSim, SBS: selfBERTScore, E-4: Entropy-4, D-4: Distinct-4, SB-3: self-BLEU3, HM: Harmonic Mean. Best results for each metric are shown in italics, while the best performing ICL results are shown in bold. Method SCS \u21d3 SBS \u21d3 E-4\u21d1 D-4\u21d1 SB-3\u21d3 BLEU-3\u21d1 SPICE\u21d1 HM \u21d1 FBD \u21d3 self-BLEU-3 83.5 66.2 11.0 88.5 31.0 47.4 29.1 72.7 51.8 self-CosSim 81.0 70.1 10.9 82.5 44.5 47.6 29.3 65.7 51.8 self-BERTScore 83.1 62.8 11.0 87.0 36.3 46.5 28.9 69.6 51.8 Table 3: Comparing the effect of using different diversity metrics, f, in Algorithm 1 for ICD. We use GPT3.5-turbo as the LLM and the best results on CommonGen dataset are in bold. Here, SCS: self-CosSim, SBS: self-BERTScore, E-4: Entropy-4, D-4: Distinct-4, SB-3: self-BLEU3, HM: Harmonic Mean. methods that use Vicuna-13b to be more diverse compared to those that use GPT3.5-turbo, while the latter showing better generation quality. In Table 3, we use different diversity metrics as f in Algorithm 1 to study the effect on text generation of ICD. We see that self-BLUE-3 and self-CosSim perform similarly across the quality metrics. SelfBERTScore shows a slightly lower quality (BLEU3 and SPICE), which indicates some level of overfitting to the diversity metric being used. According to the combined metrics, any of those diversity metrics can be used with ICD to obtain comparable performance. Semantic Diversity \u21d3 Corpus Diversity \u21d1 Pairwise Diversity \u21d3 Quality \u21d1 Combined self-cosSim self-BERTScore Entropy-4 Distinct-4 self-BLEU-3 self-BLEU-4 BLEU-3 BLEU-4 SPICE BERTScore Harmonic Mean \u21d1 FBD \u21d3 KG-BART 42.1 30.9 32.7 EKI-BART 46.0 36.1 33.4 KFCNet-w/o FC 50.2 42.0 35.9 KFCNet 57.3 51.5 39.1 MoE 89.3 81.9 9.7 61.6 63.1 56.6 49.0 38.5 33.5 70.6 53.8 61.7 MoKGE 88.7 80.6 9.9 65.2 60.4 53.6 48.8 38.4 33.1 70.3 55.9 60.8 default+MoE 90.8 84.2 9.7 61.2 65.6 58.8 51.8 41.3 34.7 73.1 52.7 61.9 diversified+MoE 85.3 79.9 9.8 63.2 58.3 52.6 51.4 41.4 34.6 71.6 57.0 54.5 ICD+MoE 90.4 82.3 9.8 64.9 58.4 50.5 53.2 43.1 35.4 73.8 59.3 62.5 Table 4: Downstream evaluation of the LLM-generated sentences. Top block methods use human-generated resources for training, while the ones in the bottom block are trained on LLM-generated sentences. MoE approaches are shown in the middle block and bottom block. BART-large is used as the generator for MoE-based methods. Best results for each metric are shown in bold, while the best performing MoE for quality is shown in underline. Figure 3: Human vs. GPT3.5 diversity ratings for randomly sampled sets of sentences generated by ICD. Cohen\u2019s \u03ba = 0.409 indicates a moderate agreement. 5.2 Downstream Evaluation The experiments presented in \u00a7 5.1 show the ability of our proposed ICD to generate diverse and commonsense bearing sentences. Therefore, an important question with practical implications is whether we can use the sentences generated by ICD as additional training data to improve both diversity and quality of previously proposed models on the GCR task, which could be seen as a downstream (extrinsic) evaluation. For this purpose we select the MoE (Shen et al., 2019), which diversifies the generation by selecting outputs from a mixture of experts. Each expert is assigned a randomly generated sequence of tokens, which is used as a prefix for all inputs sent to that expert. For each input, an expert is selected according to the value of a latent variable, which is trained using the hard-EM algorithm. We follow Liu et al. (2023) and train three experts that retrieve sentences from the collection of sentences generated by ICD for concept sets in the CommonGen train split (210846 sentences in total). We use BART-large (Lewis et al., 2020) as the base model, which has shown to produce high quality commonsense generations (Zhang et al., 2023) as the generator for all experts (see Appendix A for further details). We denote this method by ICD+MoE. As baselines for comparisons, we repeat the above process using the sentences generated by default and diversified, which we denote respectively as default+MoE and diversified+MoE in Table 4. Moreover, we compare the performance against two previously proposed MoE models: MoE (Shen et al., 2019) and MoKGE (Yu et al., 2022). MoE relies solely on the base model, whereas MoKGE requires each expert to use different sets of concepts from the ConceptNet (Speer et al., 2017) knowledge graph (KG). Because Yu et al. (2022) do not evaluate their MoKGE method on CommonGen, we ran their original implementation6 on CommonGen and report results in Table 4. All previously proposed GCR methods are exclusively trained using human-created data (e.g. sentences written by human and/or manually compiled KGs such as ConceptNet), whereas the methods described thus far in this section are trained on sentences generated by an LLM (GPT3.5-turbo). Therefore, to evaluate the feasibility of using LLMgenerated sentences for training GCR models, we include the following previously proposed GCR models that are trained using a combination of corpora and KGs: KG-BART (Liu et al., 2021),EKIBART (Fan et al., 2020) and KFCNet (Li et al., 2021). For KFCNet, we present its two results \u2013 KFCNet w/o FC, which relies only on sentences including the input concepts, without further processing, and KFCNet, which additionally ranks candidates and adds contrastive modules for the encoder and the decoder (Li et al., 2021). However, note that those methods do not consider diversifica6https://github.com/DM2-ND/MoKGE Human: \u2022 The group will use the tool to make a piece of art out of metal. \u2022 I use a tool to cut a piece of metal out of the car. \u2022 The man used a piece of metal and the tools. Default: \u2022 A piece of metal is being used as a tool. \u2022 A piece of metal was used as a tool in the construction project. \u2022 A metal tool is being used to shape a piece of metal. ICD: \u2022 The piece of metal is essential for any handyman's toolkit. \u2022 The metal tool is a useful piece for working with metal. \u2022 With the right tools, any piece of metal can be transformed  into something useful. CommonGen: Input: (piece, use, tool, metal) Human: \u2022 No one can digest electronic goods.  \u2022 Electronic products must not be eaten.  \u2022 You would die if you ate electronics. Default: \u2022 Electronic goods are not edible and are not meant for consumption. \u2022 Electronic goods are not edible and cannot be consumed as food. \u2022 Electronic goods are not edible and are meant for functional use rather  than consumption. ICD: \u2022 Eating electronic goods can damage the digestive system and cause  serious health issues. \u2022 It is not healthy or safe to eat electronic goods as they are made up of  toxic materials. \u2022 Electronic goods are not edible and cannot be consumed as food. ComVE: Input: My friend like to eat electronic goods. Figure 4: Sentences generated by default prompt and ICD against those by humans on CommonGen and ComVE test instances. ICD generates more diverse and high quality sentences than default. tion, and do not report performance using diversity metrics. Therefore, we report only their published results for generation quality in Table 4. From Table 4 we see that diversified+MoE always outperforms the original MoE in all diversity metrics, which shows that sentences generated from LLMs can be used to diversify MoE-based GCR. ICD+MoE closely matches the performance of diversified+MoE on diversity metrics, while consistently outperforming both diversified+MoE and default+MoE on quality metrics. In particular, the quality metrics reported by ICD+MoE (underlined in Table 4) are competitive against those obtained by the models that are trained on human-compiled resources (in the top block), except against KFCNet. This finding hints at potential improvement gains for GCR by using hybrid training resources that combine both human-compiled and LLM-generated data, which we highlight as an interesting future research direction. 5.3 Diversity-Awareness of LLMs Given that we use LLMs to produce diverse generations via ICL, it remains an open question whether an LLM would agree with humans on the diversity of a given set of sentences. To answer this question, we use randomly selected 210 sentences (35 sets, each containing 6 sentences) generated by ICD (using self-BLEU-3 as the diversity metric) for the input concept sets in the CommonGen dataset. We instruct GPT3.5-turbo to rate the diversity of a given set of sentences according to five diversity ratings 1-5 with 1 being highly similar, while 5 being highly diverse.7 We provide the same instruction as the annotation guidelines for eight 7Detailed prompt templates are shown in Appendix E. human-annotators, who are graduate students in NLP. To reduce the subjective variability in human judgements, we average and then normalise the ratings following the Likert scale. In Figure 3, we plot the GPT-assigned ratings against those by humans. We further split the ratings into high vs. low diversity ratings depending on whether the rating is greater or lesser than 3. The majority of the data points are distributed along the diagonal quadrants and a Cohen\u2019s Kappa of 0.409 indicating a moderate level of agreement between GPT and human ratings for diversity. The generated sentences using the default prompt, ICD and the human references in CommonGen and ComVE datasets for a single test instance are shown in Figure 4. From Figure 4 we see that the sentences generated using the default prompt often results in significant token overlap, thereby lowering the diversity. On the other hand, ICD generates both lexically and semantically diverse sentences, covering the diverse viewpoints in the human references. 6 Conclusion We proposed, ICD, an ICL-based method for achieving the optimal balance between diversity and quality in text generation via LLMs. Our experiments, conducted on three GCR tasks, demonstrate that ICD significantly improves the diversity without substantially compromising the quality. Furthermore, we found that by training on the sentences generated by ICD, we can improve diversity in previously proposed GCR methods. 7 Limitations This study primarily focuses on the generation of English sentences using pre-trained LLMs, a limitation shaped by the datasets we employed. Specifically, we used the ComVE (Wang et al., 2020), CommonGen (Lin et al., 2020) and DimonGen (Liu et al., 2023) datasets, which are well-regarded for evaluating diversified commonsense reasoning in English. Therefore, our evaluation of the generation quality was limited to English, which is a morphologically limited language. Future research could expand this scope to include multilingual pretrained models, thereby encompassing a broader linguistic spectrum. Our approach is primarily geared towards optimizing the trade-off between diversity and quality in text generation. Consequently, we maintained consistent default instructions across all experiments, adopting the standard commonsense generation prompts used in Li et al. (2023) as our default instructions. We conducted our experiments using both a closed model (i.e. GPT3.5-turbo) as well as an open-source one (i.e. Vicuna-13b-v1.5) to promote the reproducibility of our results, which are reported using multiple public available benchmarks. However, there exist many other LLMs with varying numbers of parameters and trained on different corpora. Therefore, we consider it is important to evaluate our proposed method on a broad range of LLMs to verify the generalisability of our proposed method. However, conducting such a broad analysis can be computationally costly and expensive. For example, although GPT-4 is known to have superior text generation capabilities, it incurs substantially greater costs (being 30 times more expensive than GPT3.5-turbo at the current pricing). Nevertheless, ICD is adaptable and could be extended to other LLMs. 8 Ethical Considerations In this work, we did not create or release any manually annotated data. Our work is based on the publicly available datasets, CommonGen, ComVE, and DimonGen. To the best of our knowledge, no ethical issues have been reported for those datasets. Therefore, we do not foresee any data-related ethical issues arising from our work. However, LLMs are known to generate responses that may reflect societal biases and potentially harmful content. We have not verified whether the GPT3.5-turbo and Vicuna-13b LLMs that we use in our experiments have similar problems. Therefore, it is important to test on existing benchmarks for social biases and harmful generations before the proposed method is deployed to diversify existing GCR methods used by human users. To elicit human judgements of diversity for the sentences generated by ICD, we use annotators who are familiar with working with LLMs. It is possible that their subjective (and possibly biased) viewpoints might have influenced the ratings provided. Therefore, it will be important to conduct the evaluation involving a group of annotators with different backgrounds to validate the findings reported in this analysis."
    },
    {
        "url": "http://arxiv.org/abs/2305.14160v4",
        "title": "Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learning",
        "abstract": "In-context learning (ICL) emerges as a promising capability of large language\nmodels (LLMs) by providing them with demonstration examples to perform diverse\ntasks. However, the underlying mechanism of how LLMs learn from the provided\ncontext remains under-explored. In this paper, we investigate the working\nmechanism of ICL through an information flow lens. Our findings reveal that\nlabel words in the demonstration examples function as anchors: (1) semantic\ninformation aggregates into label word representations during the shallow\ncomputation layers' processing; (2) the consolidated information in label words\nserves as a reference for LLMs' final predictions. Based on these insights, we\nintroduce an anchor re-weighting method to improve ICL performance, a\ndemonstration compression technique to expedite inference, and an analysis\nframework for diagnosing ICL errors in GPT2-XL. The promising applications of\nour findings again validate the uncovered ICL working mechanism and pave the\nway for future studies.",
        "authors": "Lean Wang, Lei Li, Damai Dai, Deli Chen, Hao Zhou, Fandong Meng, Jie Zhou, Xu Sun",
        "published": "2023-05-23",
        "updated": "2023-12-19",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.LG"
        ],
        "label": "Related Work"
    },
    {
        "url": "http://arxiv.org/abs/2307.09288v2",
        "title": "Llama 2: Open Foundation and Fine-Tuned Chat Models",
        "abstract": "In this work, we develop and release Llama 2, a collection of pretrained and\nfine-tuned large language models (LLMs) ranging in scale from 7 billion to 70\nbillion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for\ndialogue use cases. Our models outperform open-source chat models on most\nbenchmarks we tested, and based on our human evaluations for helpfulness and\nsafety, may be a suitable substitute for closed-source models. We provide a\ndetailed description of our approach to fine-tuning and safety improvements of\nLlama 2-Chat in order to enable the community to build on our work and\ncontribute to the responsible development of LLMs.",
        "authors": "Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom",
        "published": "2023-07-18",
        "updated": "2023-07-19",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI"
        ],
        "label": "Related Work"
    },
    {
        "url": "http://arxiv.org/abs/2212.10545v2",
        "title": "DimonGen: Diversified Generative Commonsense Reasoning for Explaining Concept Relationships",
        "abstract": "In this paper, we propose DimonGen, which aims to generate diverse sentences\ndescribing concept relationships in various everyday scenarios. To support\nthis, we first create a benchmark dataset for this task by adapting the\nexisting CommonGen dataset. We then propose a two-stage model called MoREE to\ngenerate the target sentences. MoREE consists of a mixture of retrievers model\nthat retrieves diverse context sentences related to the given concepts, and a\nmixture of generators model that generates diverse sentences based on the\nretrieved contexts. We conduct experiments on the DimonGen task and show that\nMoREE outperforms strong baselines in terms of both the quality and diversity\nof the generated sentences. Our results demonstrate that MoREE is able to\ngenerate diverse sentences that reflect different relationships between\nconcepts, leading to a comprehensive understanding of concept relationships.",
        "authors": "Chenzhengyi Liu, Jie Huang, Kerui Zhu, Kevin Chen-Chuan Chang",
        "published": "2022-12-20",
        "updated": "2023-05-16",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI"
        ],
        "label": "Related Work"
    },
    {
        "url": "http://arxiv.org/abs/2203.07285v1",
        "title": "Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph Experts",
        "abstract": "Generative commonsense reasoning (GCR) in natural language is to reason about\nthe commonsense while generating coherent text. Recent years have seen a surge\nof interest in improving the generation quality of commonsense reasoning tasks.\nNevertheless, these approaches have seldom investigated diversity in the GCR\ntasks, which aims to generate alternative explanations for a real-world\nsituation or predict all possible outcomes. Diversifying GCR is challenging as\nit expects to generate multiple outputs that are not only semantically\ndifferent but also grounded in commonsense knowledge. In this paper, we propose\nMoKGE, a novel method that diversifies the generative reasoning by a mixture of\nexpert (MoE) strategy on commonsense knowledge graphs (KG). A set of knowledge\nexperts seek diverse reasoning on KG to encourage various generation outputs.\nEmpirical experiments demonstrated that MoKGE can significantly improve the\ndiversity while achieving on par performance on accuracy on two GCR benchmarks,\nbased on both automatic and human evaluations.",
        "authors": "Wenhao Yu, Chenguang Zhu, Lianhui Qin, Zhihan Zhang, Tong Zhao, Meng Jiang",
        "published": "2022-03-14",
        "updated": "2022-03-14",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI"
        ],
        "label": "Related Work"
    },
    {
        "url": "http://arxiv.org/abs/1805.04833v1",
        "title": "Hierarchical Neural Story Generation",
        "abstract": "We explore story generation: creative systems that can build coherent and\nfluent passages of text about a topic. We collect a large dataset of 300K\nhuman-written stories paired with writing prompts from an online forum. Our\ndataset enables hierarchical story generation, where the model first generates\na premise, and then transforms it into a passage of text. We gain further\nimprovements with a novel form of model fusion that improves the relevance of\nthe story to the prompt, and adding a new gated multi-scale self-attention\nmechanism to model long-range context. Experiments show large improvements over\nstrong baselines on both automated and human evaluations. Human judges prefer\nstories generated by our approach to those from a strong non-hierarchical model\nby a factor of two to one.",
        "authors": "Angela Fan, Mike Lewis, Yann Dauphin",
        "published": "2018-05-13",
        "updated": "2018-05-13",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL"
        ],
        "label": "Related Work"
    },
    {
        "url": "http://arxiv.org/abs/1904.09751v2",
        "title": "The Curious Case of Neural Text Degeneration",
        "abstract": "Despite considerable advancements with deep neural language models, the\nenigma of neural text degeneration persists when these models are tested as\ntext generators. The counter-intuitive empirical observation is that even\nthough the use of likelihood as training objective leads to high quality models\nfor a broad range of language understanding tasks, using likelihood as a\ndecoding objective leads to text that is bland and strangely repetitive.\n  In this paper, we reveal surprising distributional differences between human\ntext and machine text. In addition, we find that decoding strategies alone can\ndramatically effect the quality of machine text, even when generated from\nexactly the same neural language model. Our findings motivate Nucleus Sampling,\na simple but effective method to draw the best out of neural generation. By\nsampling text from the dynamic nucleus of the probability distribution, which\nallows for diversity while effectively truncating the less reliable tail of the\ndistribution, the resulting text better demonstrates the quality of human text,\nyielding enhanced diversity without sacrificing fluency and coherence.",
        "authors": "Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, Yejin Choi",
        "published": "2019-04-22",
        "updated": "2020-02-14",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL"
        ],
        "label": "Related Work"
    },
    {
        "url": "http://arxiv.org/abs/2404.08517v1",
        "title": "Online Safety Analysis for LLMs: a Benchmark, an Assessment, and a Path Forward",
        "abstract": "While Large Language Models (LLMs) have seen widespread applications across\nnumerous fields, their limited interpretability poses concerns regarding their\nsafe operations from multiple aspects, e.g., truthfulness, robustness, and\nfairness. Recent research has started developing quality assurance methods for\nLLMs, introducing techniques such as offline detector-based or uncertainty\nestimation methods. However, these approaches predominantly concentrate on\npost-generation analysis, leaving the online safety analysis for LLMs during\nthe generation phase an unexplored area. To bridge this gap, we conduct in this\nwork a comprehensive evaluation of the effectiveness of existing online safety\nanalysis methods on LLMs. We begin with a pilot study that validates the\nfeasibility of detecting unsafe outputs in the early generation process.\nFollowing this, we establish the first publicly available benchmark of online\nsafety analysis for LLMs, including a broad spectrum of methods, models, tasks,\ndatasets, and evaluation metrics. Utilizing this benchmark, we extensively\nanalyze the performance of state-of-the-art online safety analysis methods on\nboth open-source and closed-source LLMs. This analysis reveals the strengths\nand weaknesses of individual methods and offers valuable insights into\nselecting the most appropriate method based on specific application scenarios\nand task requirements. Furthermore, we also explore the potential of using\nhybridization methods, i.e., combining multiple methods to derive a collective\nsafety conclusion, to enhance the efficacy of online safety analysis for LLMs.\nOur findings indicate a promising direction for the development of innovative\nand trustworthy quality assurance methodologies for LLMs, facilitating their\nreliable deployments across diverse domains.",
        "authors": "Xuan Xie, Jiayang Song, Zhehua Zhou, Yuheng Huang, Da Song, Lei Ma",
        "published": "2024-04-12",
        "updated": "2024-04-12",
        "primary_cat": "cs.SE",
        "cats": [
            "cs.SE",
            "cs.AI",
            "cs.CL",
            "cs.CR",
            "cs.LG"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2405.01769v1",
        "title": "A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law",
        "abstract": "In the fast-evolving domain of artificial intelligence, large language models\n(LLMs) such as GPT-3 and GPT-4 are revolutionizing the landscapes of finance,\nhealthcare, and law: domains characterized by their reliance on professional\nexpertise, challenging data acquisition, high-stakes, and stringent regulatory\ncompliance. This survey offers a detailed exploration of the methodologies,\napplications, challenges, and forward-looking opportunities of LLMs within\nthese high-stakes sectors. We highlight the instrumental role of LLMs in\nenhancing diagnostic and treatment methodologies in healthcare, innovating\nfinancial analytics, and refining legal interpretation and compliance\nstrategies. Moreover, we critically examine the ethics for LLM applications in\nthese fields, pointing out the existing ethical concerns and the need for\ntransparent, fair, and robust AI systems that respect regulatory norms. By\npresenting a thorough review of current literature and practical applications,\nwe showcase the transformative impact of LLMs, and outline the imperative for\ninterdisciplinary cooperation, methodological advancements, and ethical\nvigilance. Through this lens, we aim to spark dialogue and inspire future\nresearch dedicated to maximizing the benefits of LLMs while mitigating their\nrisks in these precision-dependent sectors. To facilitate future research on\nLLMs in these critical societal domains, we also initiate a reading list that\ntracks the latest advancements under this topic, which will be continually\nupdated: \\url{https://github.com/czyssrs/LLM_X_papers}.",
        "authors": "Zhiyu Zoey Chen, Jing Ma, Xinlu Zhang, Nan Hao, An Yan, Armineh Nourbakhsh, Xianjun Yang, Julian McAuley, Linda Petzold, William Yang Wang",
        "published": "2024-05-02",
        "updated": "2024-05-02",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2404.10199v3",
        "title": "CULTURE-GEN: Revealing Global Cultural Perception in Language Models through Natural Language Prompting",
        "abstract": "As the utilization of large language models (LLMs) has proliferated\nworldwide, it is crucial for them to have adequate knowledge and fair\nrepresentation for diverse global cultures. In this work, we uncover culture\nperceptions of three SOTA models on 110 countries and regions on 8\nculture-related topics through culture-conditioned generations, and extract\nsymbols from these generations that are associated to each culture by the LLM.\nWe discover that culture-conditioned generation consist of linguistic \"markers\"\nthat distinguish marginalized cultures apart from default cultures. We also\ndiscover that LLMs have an uneven degree of diversity in the culture symbols,\nand that cultures from different geographic regions have different presence in\nLLMs' culture-agnostic generation. Our findings promote further research in\nstudying the knowledge and fairness of global culture perception in LLMs. Code\nand Data can be found in: https://github.com/huihanlhh/Culture-Gen/",
        "authors": "Huihan Li, Liwei Jiang, Jena D. Huang, Hyunwoo Kim, Sebastin Santy, Taylor Sorensen, Bill Yuchen Lin, Nouha Dziri, Xiang Ren, Yejin Choi",
        "published": "2024-04-16",
        "updated": "2024-04-26",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2308.10397v2",
        "title": "FairMonitor: A Four-Stage Automatic Framework for Detecting Stereotypes and Biases in Large Language Models",
        "abstract": "Detecting stereotypes and biases in Large Language Models (LLMs) can enhance\nfairness and reduce adverse impacts on individuals or groups when these LLMs\nare applied. However, the majority of existing methods focus on measuring the\nmodel's preference towards sentences containing biases and stereotypes within\ndatasets, which lacks interpretability and cannot detect implicit biases and\nstereotypes in the real world. To address this gap, this paper introduces a\nfour-stage framework to directly evaluate stereotypes and biases in the\ngenerated content of LLMs, including direct inquiry testing, serial or adapted\nstory testing, implicit association testing, and unknown situation testing.\nAdditionally, the paper proposes multi-dimensional evaluation metrics and\nexplainable zero-shot prompts for automated evaluation. Using the education\nsector as a case study, we constructed the Edu-FairMonitor based on the\nfour-stage framework, which encompasses 12,632 open-ended questions covering\nnine sensitive factors and 26 educational scenarios. Experimental results\nreveal varying degrees of stereotypes and biases in five LLMs evaluated on\nEdu-FairMonitor. Moreover, the results of our proposed automated evaluation\nmethod have shown a high correlation with human annotations.",
        "authors": "Yanhong Bai, Jiabao Zhao, Jinxin Shi, Tingjiang Wei, Xingjiao Wu, Liang He",
        "published": "2023-08-21",
        "updated": "2023-10-27",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2403.15491v1",
        "title": "Open Source Conversational LLMs do not know most Spanish words",
        "abstract": "The growing interest in Large Language Models (LLMs) and in particular in\nconversational models with which users can interact has led to the development\nof a large number of open-source chat LLMs. These models are evaluated on a\nwide range of benchmarks to assess their capabilities in answering questions or\nsolving problems on almost any possible topic or to test their ability to\nreason or interpret texts. Instead, the evaluation of the knowledge that these\nmodels have of the languages has received much less attention. For example, the\nwords that they can recognize and use in different languages. In this paper, we\nevaluate the knowledge that open-source chat LLMs have of Spanish words by\ntesting a sample of words in a reference dictionary. The results show that\nopen-source chat LLMs produce incorrect meanings for an important fraction of\nthe words and are not able to use most of the words correctly to write\nsentences with context. These results show how Spanish is left behind in the\nopen-source LLM race and highlight the need to push for linguistic fairness in\nconversational LLMs ensuring that they provide similar performance across\nlanguages.",
        "authors": "Javier Conde, Miguel Gonz\u00e1lez, Nina Melero, Raquel Ferrando, Gonzalo Mart\u00ednez, Elena Merino-G\u00f3mez, Jos\u00e9 Alberto Hern\u00e1ndez, Pedro Reviriego",
        "published": "2024-03-21",
        "updated": "2024-03-21",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2311.18580v1",
        "title": "FFT: Towards Harmlessness Evaluation and Analysis for LLMs with Factuality, Fairness, Toxicity",
        "abstract": "The widespread of generative artificial intelligence has heightened concerns\nabout the potential harms posed by AI-generated texts, primarily stemming from\nfactoid, unfair, and toxic content. Previous researchers have invested much\neffort in assessing the harmlessness of generative language models. However,\nexisting benchmarks are struggling in the era of large language models (LLMs),\ndue to the stronger language generation and instruction following capabilities,\nas well as wider applications. In this paper, we propose FFT, a new benchmark\nwith 2116 elaborated-designed instances, for LLM harmlessness evaluation with\nfactuality, fairness, and toxicity. To investigate the potential harms of LLMs,\nwe evaluate 9 representative LLMs covering various parameter scales, training\nstages, and creators. Experiments show that the harmlessness of LLMs is still\nunder-satisfactory, and extensive analysis derives some insightful findings\nthat could inspire future research for harmless LLM research.",
        "authors": "Shiyao Cui, Zhenyu Zhang, Yilong Chen, Wenyuan Zhang, Tianyun Liu, Siqi Wang, Tingwen Liu",
        "published": "2023-11-30",
        "updated": "2023-11-30",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.CR"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2402.08189v1",
        "title": "Simulating Human Strategic Behavior: Comparing Single and Multi-agent LLMs",
        "abstract": "When creating plans, policies, or applications for people, it is challenging\nfor designers to think through the strategic ways that different people will\nbehave. Recently, Large Language Models (LLMs) have been shown to create\nrealistic simulations of human-like behavior based on personas. We build on\nthis to investigate whether LLMs can simulate human strategic behavior. Human\nstrategies are complex because they take into account social norms in addition\nto aiming to maximize personal gain. The ultimatum game is a classic economics\nexperiment used to understand human strategic behavior in a social setting. It\nshows that people will often choose to \"punish\" other players to enforce social\nnorms rather than to maximize personal profits. We test whether LLMs can\nreplicate this complex behavior in simulations. We compare two architectures:\nsingle- and multi-agent LLMs. We compare their abilities to (1) simulate\nhuman-like actions in the ultimatum game, (2) simulate two player\npersonalities, greedy and fair, and (3) create robust strategies that are\nlogically complete and consistent with personality. Our evaluation shows the\nmulti-agent architecture is much more accurate than single LLMs (88% vs. 50%)\nin simulating human strategy creation and actions for personality pairs. Thus\nthere is potential to use LLMs to simulate human strategic behavior to help\ndesigners, planners, and policymakers perform preliminary exploration of how\npeople behave in systems.",
        "authors": "Karthik Sreedhar, Lydia Chilton",
        "published": "2024-02-13",
        "updated": "2024-02-13",
        "primary_cat": "cs.HC",
        "cats": [
            "cs.HC"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2310.05694v1",
        "title": "A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics",
        "abstract": "The utilization of large language models (LLMs) in the Healthcare domain has\ngenerated both excitement and concern due to their ability to effectively\nrespond to freetext queries with certain professional knowledge. This survey\noutlines the capabilities of the currently developed LLMs for Healthcare and\nexplicates their development process, with the aim of providing an overview of\nthe development roadmap from traditional Pretrained Language Models (PLMs) to\nLLMs. Specifically, we first explore the potential of LLMs to enhance the\nefficiency and effectiveness of various Healthcare applications highlighting\nboth the strengths and limitations. Secondly, we conduct a comparison between\nthe previous PLMs and the latest LLMs, as well as comparing various LLMs with\neach other. Then we summarize related Healthcare training data, training\nmethods, optimization strategies, and usage. Finally, the unique concerns\nassociated with deploying LLMs in Healthcare settings are investigated,\nparticularly regarding fairness, accountability, transparency and ethics. Our\nsurvey provide a comprehensive investigation from perspectives of both computer\nscience and Healthcare specialty. Besides the discussion about Healthcare\nconcerns, we supports the computer science community by compiling a collection\nof open source resources, such as accessible datasets, the latest\nmethodologies, code implementations, and evaluation benchmarks in the Github.\nSummarily, we contend that a significant paradigm shift is underway,\ntransitioning from PLMs to LLMs. This shift encompasses a move from\ndiscriminative AI approaches to generative AI approaches, as well as a shift\nfrom model-centered methodologies to datacentered methodologies.",
        "authors": "Kai He, Rui Mao, Qika Lin, Yucheng Ruan, Xiang Lan, Mengling Feng, Erik Cambria",
        "published": "2023-10-09",
        "updated": "2023-10-09",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2404.12736v1",
        "title": "Large Language Model Supply Chain: A Research Agenda",
        "abstract": "The rapid advancements in pre-trained Large Language Models (LLMs) and Large\nMultimodal Models (LMMs) have ushered in a new era of intelligent applications,\ntransforming fields ranging from natural language processing to content\ngeneration. The LLM supply chain represents a crucial aspect of the\ncontemporary artificial intelligence landscape. It encompasses the entire\nlifecycle of pre-trained models, from its initial development and training to\nits final deployment and application in various domains. This paper presents a\ncomprehensive overview of the LLM supply chain, highlighting its three core\nelements: 1) the model infrastructure, encompassing datasets and toolchain for\ntraining, optimization, and deployment; 2) the model lifecycle, covering\ntraining, testing, releasing, and ongoing maintenance; and 3) the downstream\napplication ecosystem, enabling the integration of pre-trained models into a\nwide range of intelligent applications. However, this rapidly evolving field\nfaces numerous challenges across these key components, including data privacy\nand security, model interpretability and fairness, infrastructure scalability,\nand regulatory compliance. Addressing these challenges is essential for\nharnessing the full potential of LLMs and ensuring their ethical and\nresponsible use. This paper provides a future research agenda for the LLM\nsupply chain, aiming at driving the continued advancement and responsible\ndeployment of these transformative LLMs.",
        "authors": "Shenao Wang, Yanjie Zhao, Xinyi Hou, Haoyu Wang",
        "published": "2024-04-19",
        "updated": "2024-04-19",
        "primary_cat": "cs.SE",
        "cats": [
            "cs.SE"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2308.11483v1",
        "title": "Large Language Models Sensitivity to The Order of Options in Multiple-Choice Questions",
        "abstract": "Large Language Models (LLMs) have demonstrated remarkable capabilities in\nvarious NLP tasks. However, previous works have shown these models are\nsensitive towards prompt wording, and few-shot demonstrations and their order,\nposing challenges to fair assessment of these models. As these models become\nmore powerful, it becomes imperative to understand and address these\nlimitations. In this paper, we focus on LLMs robustness on the task of\nmultiple-choice questions -- commonly adopted task to study reasoning and\nfact-retrieving capability of LLMs. Investigating the sensitivity of LLMs\ntowards the order of options in multiple-choice questions, we demonstrate a\nconsiderable performance gap of approximately 13% to 75% in LLMs on different\nbenchmarks, when answer options are reordered, even when using demonstrations\nin a few-shot setting. Through a detailed analysis, we conjecture that this\nsensitivity arises when LLMs are uncertain about the prediction between the\ntop-2/3 choices, and specific options placements may favor certain prediction\nbetween those top choices depending on the question caused by positional bias.\nWe also identify patterns in top-2 choices that amplify or mitigate the model's\nbias toward option placement. We found that for amplifying bias, the optimal\nstrategy involves positioning the top two choices as the first and last\noptions. Conversely, to mitigate bias, we recommend placing these choices among\nthe adjacent options. To validate our conjecture, we conduct various\nexperiments and adopt two approaches to calibrate LLMs' predictions, leading to\nup to 8 percentage points improvement across different models and benchmarks.",
        "authors": "Pouya Pezeshkpour, Estevam Hruschka",
        "published": "2023-08-22",
        "updated": "2023-08-22",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI",
            "cs.LG"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2309.14345v2",
        "title": "Bias Testing and Mitigation in LLM-based Code Generation",
        "abstract": "Utilizing state-of-the-art Large Language Models (LLMs), automatic code\ngeneration models play a pivotal role in enhancing the productivity of software\ndevelopment procedures. As the adoption of LLMs becomes more widespread in\nsoftware coding ecosystems, a pressing issue has emerged: does the generated\ncode contain social bias and unfairness, such as those related to age, gender,\nand race? This issue concerns the integrity, fairness, and ethical foundation\nof software applications that depend on the code generated by these models, yet\nis under-explored in the literature. This paper presents a novel bias testing\nframework that is specifically designed for code generation tasks. Based on\nthis framework, we conduct an extensive evaluation of the bias in code\ngenerated by five state-of-the-art LLMs. Our findings reveal that 20.29% to\n44.93% code functions generated by the models under study are biased when\nhandling bias sensitive tasks (i.e., tasks that involve sensitive attributes\nsuch as age and gender). This indicates that the existing LLMs can be unfair in\ncode generation, posing risks of unintended and harmful software behaviors. To\nmitigate bias for code generation models, we evaluate five bias mitigation\nprompt strategies, i.e., utilizing bias testing results to refine the code\n(zero-shot), one-, few-shot, and two Chain-of-Thought (CoT) prompts. Our\nevaluation results illustrate that these strategies are all effective in\nmitigating bias. Overall, one-shot and few-shot learning are the two most\neffective. For GPT-4, 80% to 90% code bias can be removed with one-shot\nlearning.",
        "authors": "Dong Huang, Qingwen Bu, Jie Zhang, Xiaofei Xie, Junjie Chen, Heming Cui",
        "published": "2023-09-03",
        "updated": "2024-01-09",
        "primary_cat": "cs.SE",
        "cats": [
            "cs.SE",
            "cs.AI"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2310.18130v2",
        "title": "DELPHI: Data for Evaluating LLMs' Performance in Handling Controversial Issues",
        "abstract": "Controversy is a reflection of our zeitgeist, and an important aspect to any\ndiscourse. The rise of large language models (LLMs) as conversational systems\nhas increased public reliance on these systems for answers to their various\nquestions. Consequently, it is crucial to systematically examine how these\nmodels respond to questions that pertaining to ongoing debates. However, few\nsuch datasets exist in providing human-annotated labels reflecting the\ncontemporary discussions. To foster research in this area, we propose a novel\nconstruction of a controversial questions dataset, expanding upon the publicly\nreleased Quora Question Pairs Dataset. This dataset presents challenges\nconcerning knowledge recency, safety, fairness, and bias. We evaluate different\nLLMs using a subset of this dataset, illuminating how they handle controversial\nissues and the stances they adopt. This research ultimately contributes to our\nunderstanding of LLMs' interaction with controversial issues, paving the way\nfor improvements in their comprehension and handling of complex societal\ndebates.",
        "authors": "David Q. Sun, Artem Abzaliev, Hadas Kotek, Zidi Xiu, Christopher Klein, Jason D. Williams",
        "published": "2023-10-27",
        "updated": "2023-11-07",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.HC"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2310.16343v2",
        "title": "Evaluating, Understanding, and Improving Constrained Text Generation for Large Language Models",
        "abstract": "Advancements in natural language generation (NLG) and large language models\n(LLMs) have led to proficient text generation in various tasks. However,\nintegrating intricate constraints into neural text generation, due to LLMs'\nopacity, remains challenging. This study investigates constrained text\ngeneration for LLMs, where predefined constraints are applied during LLM's\ngeneration process. Our research mainly focuses on mainstream open-source LLMs,\ncategorizing constraints into lexical, structural, and relation-based types. We\nalso present various benchmarks to facilitate fair evaluation. The study\naddresses some key research questions, including evaluating, understanding and\nimproving constrained text generation for LLMs. Results illuminate LLMs'\ncapacity and deficiency to incorporate constraints and provide insights for\nfuture developments in constrained text generation. Codes and datasets will be\nreleased upon acceptance.",
        "authors": "Xiang Chen, Xiaojun Wan",
        "published": "2023-10-25",
        "updated": "2024-03-21",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2403.09606v1",
        "title": "Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey",
        "abstract": "Causal inference has shown potential in enhancing the predictive accuracy,\nfairness, robustness, and explainability of Natural Language Processing (NLP)\nmodels by capturing causal relationships among variables. The emergence of\ngenerative Large Language Models (LLMs) has significantly impacted various NLP\ndomains, particularly through their advanced reasoning capabilities. This\nsurvey focuses on evaluating and improving LLMs from a causal view in the\nfollowing areas: understanding and improving the LLMs' reasoning capacity,\naddressing fairness and safety issues in LLMs, complementing LLMs with\nexplanations, and handling multimodality. Meanwhile, LLMs' strong reasoning\ncapacities can in turn contribute to the field of causal inference by aiding\ncausal relationship discovery and causal effect estimations. This review\nexplores the interplay between causal inference frameworks and LLMs from both\nperspectives, emphasizing their collective potential to further the development\nof more advanced and equitable artificial intelligence systems.",
        "authors": "Xiaoyu Liu, Paiheng Xu, Junda Wu, Jiaxin Yuan, Yifan Yang, Yuhang Zhou, Fuxiao Liu, Tianrui Guan, Haoliang Wang, Tong Yu, Julian McAuley, Wei Ai, Furong Huang",
        "published": "2024-03-14",
        "updated": "2024-03-14",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2404.01349v1",
        "title": "Fairness in Large Language Models: A Taxonomic Survey",
        "abstract": "Large Language Models (LLMs) have demonstrated remarkable success across\nvarious domains. However, despite their promising performance in numerous\nreal-world applications, most of these algorithms lack fairness considerations.\nConsequently, they may lead to discriminatory outcomes against certain\ncommunities, particularly marginalized populations, prompting extensive study\nin fair LLMs. On the other hand, fairness in LLMs, in contrast to fairness in\ntraditional machine learning, entails exclusive backgrounds, taxonomies, and\nfulfillment techniques. To this end, this survey presents a comprehensive\noverview of recent advances in the existing literature concerning fair LLMs.\nSpecifically, a brief introduction to LLMs is provided, followed by an analysis\nof factors contributing to bias in LLMs. Additionally, the concept of fairness\nin LLMs is discussed categorically, summarizing metrics for evaluating bias in\nLLMs and existing algorithms for promoting fairness. Furthermore, resources for\nevaluating bias in LLMs, including toolkits and datasets, are summarized.\nFinally, existing research challenges and open questions are discussed.",
        "authors": "Zhibo Chu, Zichong Wang, Wenbin Zhang",
        "published": "2024-03-31",
        "updated": "2024-03-31",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2404.07981v1",
        "title": "Manipulating Large Language Models to Increase Product Visibility",
        "abstract": "Large language models (LLMs) are increasingly being integrated into search\nengines to provide natural language responses tailored to user queries.\nCustomers and end-users are also becoming more dependent on these models for\nquick and easy purchase decisions. In this work, we investigate whether\nrecommendations from LLMs can be manipulated to enhance a product's visibility.\nWe demonstrate that adding a strategic text sequence (STS) -- a carefully\ncrafted message -- to a product's information page can significantly increase\nits likelihood of being listed as the LLM's top recommendation. To understand\nthe impact of STS, we use a catalog of fictitious coffee machines and analyze\nits effect on two target products: one that seldom appears in the LLM's\nrecommendations and another that usually ranks second. We observe that the\nstrategic text sequence significantly enhances the visibility of both products\nby increasing their chances of appearing as the top recommendation. This\nability to manipulate LLM-generated search responses provides vendors with a\nconsiderable competitive advantage and has the potential to disrupt fair market\ncompetition. Just as search engine optimization (SEO) revolutionized how\nwebpages are customized to rank higher in search engine results, influencing\nLLM recommendations could profoundly impact content optimization for AI-driven\nsearch services. Code for our experiments is available at\nhttps://github.com/aounon/llm-rank-optimizer.",
        "authors": "Aounon Kumar, Himabindu Lakkaraju",
        "published": "2024-04-11",
        "updated": "2024-04-11",
        "primary_cat": "cs.IR",
        "cats": [
            "cs.IR",
            "cs.AI",
            "cs.CL"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2312.06056v1",
        "title": "METAL: Metamorphic Testing Framework for Analyzing Large-Language Model Qualities",
        "abstract": "Large-Language Models (LLMs) have shifted the paradigm of natural language\ndata processing. However, their black-boxed and probabilistic characteristics\ncan lead to potential risks in the quality of outputs in diverse LLM\napplications. Recent studies have tested Quality Attributes (QAs), such as\nrobustness or fairness, of LLMs by generating adversarial input texts. However,\nexisting studies have limited their coverage of QAs and tasks in LLMs and are\ndifficult to extend. Additionally, these studies have only used one evaluation\nmetric, Attack Success Rate (ASR), to assess the effectiveness of their\napproaches. We propose a MEtamorphic Testing for Analyzing LLMs (METAL)\nframework to address these issues by applying Metamorphic Testing (MT)\ntechniques. This approach facilitates the systematic testing of LLM qualities\nby defining Metamorphic Relations (MRs), which serve as modularized evaluation\nmetrics. The METAL framework can automatically generate hundreds of MRs from\ntemplates that cover various QAs and tasks. In addition, we introduced novel\nmetrics that integrate the ASR method into the semantic qualities of text to\nassess the effectiveness of MRs accurately. Through the experiments conducted\nwith three prominent LLMs, we have confirmed that the METAL framework\neffectively evaluates essential QAs on primary LLM tasks and reveals the\nquality risks in LLMs. Moreover, the newly proposed metrics can guide the\noptimal MRs for testing each task and suggest the most effective method for\ngenerating MRs.",
        "authors": "Sangwon Hyun, Mingyu Guo, M. Ali Babar",
        "published": "2023-12-11",
        "updated": "2023-12-11",
        "primary_cat": "cs.SE",
        "cats": [
            "cs.SE",
            "cs.AI",
            "cs.CL"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2312.15398v1",
        "title": "Fairness-Aware Structured Pruning in Transformers",
        "abstract": "The increasing size of large language models (LLMs) has introduced challenges\nin their training and inference. Removing model components is perceived as a\nsolution to tackle the large model sizes, however, existing pruning methods\nsolely focus on performance, without considering an essential aspect for the\nresponsible use of LLMs: model fairness. It is crucial to address the fairness\nof LLMs towards diverse groups, such as women, Black people, LGBTQ+, Jewish\ncommunities, among others, as they are being deployed and available to a wide\naudience. In this work, first, we investigate how attention heads impact\nfairness and performance in pre-trained transformer-based language models. We\nthen propose a novel method to prune the attention heads that negatively impact\nfairness while retaining the heads critical for performance, i.e. language\nmodeling capabilities. Our approach is practical in terms of time and\nresources, as it does not require fine-tuning the final pruned, and fairer,\nmodel. Our findings demonstrate a reduction in gender bias by 19%, 19.5%,\n39.5%, 34.7%, 23%, and 8% for DistilGPT-2, GPT-2, GPT-Neo of two different\nsizes, GPT-J, and Llama 2 models, respectively, in comparison to the biased\nmodel, with only a slight decrease in performance.",
        "authors": "Abdelrahman Zayed, Goncalo Mordido, Samira Shabanian, Ioana Baldini, Sarath Chandar",
        "published": "2023-12-24",
        "updated": "2023-12-24",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.CY",
            "cs.LG"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2403.14473v1",
        "title": "The Ethics of ChatGPT in Medicine and Healthcare: A Systematic Review on Large Language Models (LLMs)",
        "abstract": "With the introduction of ChatGPT, Large Language Models (LLMs) have received\nenormous attention in healthcare. Despite their potential benefits, researchers\nhave underscored various ethical implications. While individual instances have\ndrawn much attention, the debate lacks a systematic overview of practical\napplications currently researched and ethical issues connected to them. Against\nthis background, this work aims to map the ethical landscape surrounding the\ncurrent stage of deployment of LLMs in medicine and healthcare. Electronic\ndatabases and preprint servers were queried using a comprehensive search\nstrategy. Studies were screened and extracted following a modified rapid review\napproach. Methodological quality was assessed using a hybrid approach. For 53\nrecords, a meta-aggregative synthesis was performed. Four fields of\napplications emerged and testify to a vivid exploration phase. Advantages of\nusing LLMs are attributed to their capacity in data analysis, personalized\ninformation provisioning, support in decision-making, mitigating information\nloss and enhancing information accessibility. However, we also identifies\nrecurrent ethical concerns connected to fairness, bias, non-maleficence,\ntransparency, and privacy. A distinctive concern is the tendency to produce\nharmful misinformation or convincingly but inaccurate content. A recurrent plea\nfor ethical guidance and human oversight is evident. Given the variety of use\ncases, it is suggested that the ethical guidance debate be reframed to focus on\ndefining what constitutes acceptable human oversight across the spectrum of\napplications. This involves considering diverse settings, varying potentials\nfor harm, and different acceptable thresholds for performance and certainty in\nhealthcare. In addition, a critical inquiry is necessary to determine the\nextent to which the current experimental use of LLMs is necessary and\njustified.",
        "authors": "Joschka Haltaufderheide, Robert Ranisch",
        "published": "2024-03-21",
        "updated": "2024-03-21",
        "primary_cat": "cs.CY",
        "cats": [
            "cs.CY"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2311.04892v2",
        "title": "Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs",
        "abstract": "Recent works have showcased the ability of LLMs to embody diverse personas in\ntheir responses, exemplified by prompts like 'You are Yoda. Explain the Theory\nof Relativity.' While this ability allows personalization of LLMs and enables\nhuman behavior simulation, its effect on LLMs' capabilities remains unclear. To\nfill this gap, we present the first extensive study of the unintended\nside-effects of persona assignment on the ability of LLMs to perform basic\nreasoning tasks. Our study covers 24 reasoning datasets, 4 LLMs, and 19 diverse\npersonas (e.g. an Asian person) spanning 5 socio-demographic groups. Our\nexperiments unveil that LLMs harbor deep rooted bias against various\nsocio-demographics underneath a veneer of fairness. While they overtly reject\nstereotypes when explicitly asked ('Are Black people less skilled at\nmathematics?'), they manifest stereotypical and erroneous presumptions when\nasked to answer questions while adopting a persona. These can be observed as\nabstentions in responses, e.g., 'As a Black person, I can't answer this\nquestion as it requires math knowledge', and generally result in a substantial\nperformance drop. Our experiments with ChatGPT-3.5 show that this bias is\nubiquitous - 80% of our personas demonstrate bias; it is significant - some\ndatasets show performance drops of 70%+; and can be especially harmful for\ncertain groups - some personas suffer statistically significant drops on 80%+\nof the datasets. Overall, all 4 LLMs exhibit this bias to varying extents, with\nGPT-4-Turbo showing the least but still a problematic amount of bias (evident\nin 42% of the personas). Further analysis shows that these persona-induced\nerrors can be hard-to-discern and hard-to-avoid. Our findings serve as a\ncautionary tale that the practice of assigning personas to LLMs - a trend on\nthe rise - can surface their deep-rooted biases and have unforeseeable and\ndetrimental side-effects.",
        "authors": "Shashank Gupta, Vaishnavi Shrivastava, Ameet Deshpande, Ashwin Kalyan, Peter Clark, Ashish Sabharwal, Tushar Khot",
        "published": "2023-11-08",
        "updated": "2024-01-27",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2403.15451v1",
        "title": "Towards Enabling FAIR Dataspaces Using Large Language Models",
        "abstract": "Dataspaces have recently gained adoption across various sectors, including\ntraditionally less digitized domains such as culture. Leveraging Semantic Web\ntechnologies helps to make dataspaces FAIR, but their complexity poses a\nsignificant challenge to the adoption of dataspaces and increases their cost.\nThe advent of Large Language Models (LLMs) raises the question of how these\nmodels can support the adoption of FAIR dataspaces. In this work, we\ndemonstrate the potential of LLMs in dataspaces with a concrete example. We\nalso derive a research agenda for exploring this emerging field.",
        "authors": "Benedikt T. Arnold, Johannes Theissen-Lipp, Diego Collarana, Christoph Lange, Sandra Geisler, Edward Curry, Stefan Decker",
        "published": "2024-03-18",
        "updated": "2024-03-18",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2310.06500v1",
        "title": "MetaAgents: Simulating Interactions of Human Behaviors for LLM-based Task-oriented Coordination via Collaborative Generative Agents",
        "abstract": "Significant advancements have occurred in the application of Large Language\nModels (LLMs) for various tasks and social simulations. Despite this, their\ncapacities to coordinate within task-oriented social contexts are\nunder-explored. Such capabilities are crucial if LLMs are to effectively mimic\nhuman-like social behavior and produce meaningful results. To bridge this gap,\nwe introduce collaborative generative agents, endowing LLM-based Agents with\nconsistent behavior patterns and task-solving abilities. We situate these\nagents in a simulated job fair environment as a case study to scrutinize their\ncoordination skills. We propose a novel framework that equips collaborative\ngenerative agents with human-like reasoning abilities and specialized skills.\nOur evaluation demonstrates that these agents show promising performance.\nHowever, we also uncover limitations that hinder their effectiveness in more\ncomplex coordination tasks. Our work provides valuable insights into the role\nand evolution of LLMs in task-oriented social simulations.",
        "authors": "Yuan Li, Yixuan Zhang, Lichao Sun",
        "published": "2023-10-10",
        "updated": "2023-10-10",
        "primary_cat": "cs.AI",
        "cats": [
            "cs.AI"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2402.11406v2",
        "title": "Don't Go To Extremes: Revealing the Excessive Sensitivity and Calibration Limitations of LLMs in Implicit Hate Speech Detection",
        "abstract": "The fairness and trustworthiness of Large Language Models (LLMs) are\nreceiving increasing attention. Implicit hate speech, which employs indirect\nlanguage to convey hateful intentions, occupies a significant portion of\npractice. However, the extent to which LLMs effectively address this issue\nremains insufficiently examined. This paper delves into the capability of LLMs\nto detect implicit hate speech (Classification Task) and express confidence in\ntheir responses (Calibration Task). Our evaluation meticulously considers\nvarious prompt patterns and mainstream uncertainty estimation methods. Our\nfindings highlight that LLMs exhibit two extremes: (1) LLMs display excessive\nsensitivity towards groups or topics that may cause fairness issues, resulting\nin misclassifying benign statements as hate speech. (2) LLMs' confidence scores\nfor each method excessively concentrate on a fixed range, remaining unchanged\nregardless of the dataset's complexity. Consequently, the calibration\nperformance is heavily reliant on primary classification accuracy. These\ndiscoveries unveil new limitations of LLMs, underscoring the need for caution\nwhen optimizing models to ensure they do not veer towards extremes. This serves\nas a reminder to carefully consider sensitivity and confidence in the pursuit\nof model fairness.",
        "authors": "Min Zhang, Jianfeng He, Taoran Ji, Chang-Tien Lu",
        "published": "2024-02-18",
        "updated": "2024-02-26",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2311.13095v1",
        "title": "Enhancing Logical Reasoning in Large Language Models to Facilitate Legal Applications",
        "abstract": "Language serves as a vehicle for conveying thought, enabling communication\namong individuals. The ability to distinguish between diverse concepts,\nidentify fairness and injustice, and comprehend a range of legal notions\nfundamentally relies on logical reasoning. Large Language Models (LLMs) attempt\nto emulate human language understanding and generation, but their competency in\nlogical reasoning remains limited. This paper seeks to address the\nphilosophical question: How can we effectively teach logical reasoning to LLMs\nwhile maintaining a deep understanding of the intricate relationship between\nlanguage and logic? By focusing on bolstering LLMs' capabilities in logical\nreasoning, we aim to expand their applicability in law and other\nlogic-intensive disciplines. To this end, we propose a Reinforcement Learning\nfrom Logical Feedback (RLLF) approach, which serves as a potential framework\nfor refining LLMs' reasoning capacities. Through RLLF and a revised evaluation\nmethodology, we explore new avenues for research in this domain and contribute\nto the development of LLMs capable of handling complex legal reasoning tasks\nwhile acknowledging the fundamental connection between language and logic.",
        "authors": "Ha-Thanh Nguyen, Wachara Fungwacharakorn, Ken Satoh",
        "published": "2023-11-22",
        "updated": "2023-11-22",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2403.13840v1",
        "title": "Whose Side Are You On? Investigating the Political Stance of Large Language Models",
        "abstract": "Large Language Models (LLMs) have gained significant popularity for their\napplication in various everyday tasks such as text generation, summarization,\nand information retrieval. As the widespread adoption of LLMs continues to\nsurge, it becomes increasingly crucial to ensure that these models yield\nresponses that are politically impartial, with the aim of preventing\ninformation bubbles, upholding fairness in representation, and mitigating\nconfirmation bias. In this paper, we propose a quantitative framework and\npipeline designed to systematically investigate the political orientation of\nLLMs. Our investigation delves into the political alignment of LLMs across a\nspectrum of eight polarizing topics, spanning from abortion to LGBTQ issues.\nAcross topics, the results indicate that LLMs exhibit a tendency to provide\nresponses that closely align with liberal or left-leaning perspectives rather\nthan conservative or right-leaning ones when user queries include details\npertaining to occupation, race, or political affiliation. The findings\npresented in this study not only reaffirm earlier observations regarding the\nleft-leaning characteristics of LLMs but also surface particular attributes,\nsuch as occupation, that are particularly susceptible to such inclinations even\nwhen directly steered towards conservatism. As a recommendation to avoid these\nmodels providing politicised responses, users should be mindful when crafting\nqueries, and exercise caution in selecting neutral prompt language.",
        "authors": "Pagnarasmey Pit, Xingjun Ma, Mike Conway, Qingyu Chen, James Bailey, Henry Pit, Putrasmey Keo, Watey Diep, Yu-Gang Jiang",
        "published": "2024-03-15",
        "updated": "2024-03-15",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI",
            "cs.SI"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2401.00588v1",
        "title": "Fairness in Serving Large Language Models",
        "abstract": "High-demand LLM inference services (e.g., ChatGPT and BARD) support a wide\nrange of requests from short chat conversations to long document reading. To\nensure that all client requests are processed fairly, most major LLM inference\nservices have request rate limits, to ensure that no client can dominate the\nrequest queue. However, this rudimentary notion of fairness also results in\nunder-utilization of the resources and poor client experience when there is\nspare capacity. While there is a rich literature on fair scheduling, serving\nLLMs presents new challenges due to their unpredictable request lengths and\ntheir unique batching characteristics on parallel accelerators. This paper\nintroduces the definition of LLM serving fairness based on a cost function that\naccounts for the number of input and output tokens processed. To achieve\nfairness in serving, we propose a novel scheduling algorithm, the Virtual Token\nCounter (VTC), a fair scheduler based on the continuous batching mechanism. We\nprove a 2x tight upper bound on the service difference between two backlogged\nclients, adhering to the requirement of work-conserving. Through extensive\nexperiments, we demonstrate the superior performance of VTC in ensuring\nfairness, especially in contrast to other baseline methods, which exhibit\nshortcomings under various conditions.",
        "authors": "Ying Sheng, Shiyi Cao, Dacheng Li, Banghua Zhu, Zhuohan Li, Danyang Zhuo, Joseph E. Gonzalez, Ion Stoica",
        "published": "2023-12-31",
        "updated": "2023-12-31",
        "primary_cat": "cs.AI",
        "cats": [
            "cs.AI",
            "cs.LG",
            "cs.PF"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2309.03852v2",
        "title": "FLM-101B: An Open LLM and How to Train It with $100K Budget",
        "abstract": "Large language models (LLMs) have achieved remarkable success in NLP and\nmultimodal tasks, among others. Despite these successes, two main challenges\nremain in developing LLMs: (i) high computational cost, and (ii) fair and\nobjective evaluations. In this paper, we report a solution to significantly\nreduce LLM training cost through a growth strategy. We demonstrate that a\n101B-parameter LLM with 0.31T tokens can be trained with a budget of 100K US\ndollars. Inspired by IQ tests, we also consolidate an additional range of\nevaluations on top of existing evaluations that focus on knowledge-oriented\nabilities. These IQ evaluations include symbolic mapping, rule understanding,\npattern mining, and anti-interference. Such evaluations minimize the potential\nimpact of memorization. Experimental results show that our model, named\nFLM-101B, trained with a budget of 100K US dollars, achieves performance\ncomparable to powerful and well-known models, e.g., GPT-3 and GLM-130B,\nespecially on the additional range of IQ evaluations. The checkpoint of\nFLM-101B is released at https://huggingface.co/CofeAI/FLM-101B.",
        "authors": "Xiang Li, Yiqun Yao, Xin Jiang, Xuezhi Fang, Xuying Meng, Siqi Fan, Peng Han, Jing Li, Li Du, Bowen Qin, Zheng Zhang, Aixin Sun, Yequan Wang",
        "published": "2023-09-07",
        "updated": "2023-09-17",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2401.04057v1",
        "title": "Unveiling Bias in Fairness Evaluations of Large Language Models: A Critical Literature Review of Music and Movie Recommendation Systems",
        "abstract": "The rise of generative artificial intelligence, particularly Large Language\nModels (LLMs), has intensified the imperative to scrutinize fairness alongside\naccuracy. Recent studies have begun to investigate fairness evaluations for\nLLMs within domains such as recommendations. Given that personalization is an\nintrinsic aspect of recommendation systems, its incorporation into fairness\nassessments is paramount. Yet, the degree to which current fairness evaluation\nframeworks account for personalization remains unclear. Our comprehensive\nliterature review aims to fill this gap by examining how existing frameworks\nhandle fairness evaluations of LLMs, with a focus on the integration of\npersonalization factors. Despite an exhaustive collection and analysis of\nrelevant works, we discovered that most evaluations overlook personalization, a\ncritical facet of recommendation systems, thereby inadvertently perpetuating\nunfair practices. Our findings shed light on this oversight and underscore the\nurgent need for more nuanced fairness evaluations that acknowledge\npersonalization. Such improvements are vital for fostering equitable\ndevelopment within the AI community.",
        "authors": "Chandan Kumar Sah, Dr. Lian Xiaoli, Muhammad Mirajul Islam",
        "published": "2024-01-08",
        "updated": "2024-01-08",
        "primary_cat": "cs.IR",
        "cats": [
            "cs.IR",
            "cs.AI",
            "cs.SE"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2402.10567v3",
        "title": "InSaAF: Incorporating Safety through Accuracy and Fairness | Are LLMs ready for the Indian Legal Domain?",
        "abstract": "Recent advancements in language technology and Artificial Intelligence have\nresulted in numerous Language Models being proposed to perform various tasks in\nthe legal domain ranging from predicting judgments to generating summaries.\nDespite their immense potential, these models have been proven to learn and\nexhibit societal biases and make unfair predictions. In this study, we explore\nthe ability of Large Language Models (LLMs) to perform legal tasks in the\nIndian landscape when social factors are involved. We present a novel metric,\n$\\beta$-weighted $\\textit{Legal Safety Score ($LSS_{\\beta}$)}$, which\nencapsulates both the fairness and accuracy aspects of the LLM. We assess LLMs'\nsafety by considering its performance in the $\\textit{Binary Statutory\nReasoning}$ task and its fairness exhibition with respect to various axes of\ndisparities in the Indian society. Task performance and fairness scores of\nLLaMA and LLaMA--2 models indicate that the proposed $LSS_{\\beta}$ metric can\neffectively determine the readiness of a model for safe usage in the legal\nsector. We also propose finetuning pipelines, utilising specialised legal\ndatasets, as a potential method to mitigate bias and improve model safety. The\nfinetuning procedures on LLaMA and LLaMA--2 models increase the $LSS_{\\beta}$,\nimproving their usability in the Indian legal domain. Our code is publicly\nreleased.",
        "authors": "Yogesh Tripathi, Raghav Donakanti, Sahil Girhepuje, Ishan Kavathekar, Bhaskara Hanuma Vedula, Gokul S Krishnan, Shreya Goyal, Anmol Goel, Balaraman Ravindran, Ponnurangam Kumaraguru",
        "published": "2024-02-16",
        "updated": "2024-02-21",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2401.15585v1",
        "title": "Evaluating Gender Bias in Large Language Models via Chain-of-Thought Prompting",
        "abstract": "There exist both scalable tasks, like reading comprehension and\nfact-checking, where model performance improves with model size, and unscalable\ntasks, like arithmetic reasoning and symbolic reasoning, where model\nperformance does not necessarily improve with model size. Large language models\n(LLMs) equipped with Chain-of-Thought (CoT) prompting are able to make accurate\nincremental predictions even on unscalable tasks. Unfortunately, despite their\nexceptional reasoning abilities, LLMs tend to internalize and reproduce\ndiscriminatory societal biases. Whether CoT can provide discriminatory or\negalitarian rationalizations for the implicit information in unscalable tasks\nremains an open question.\n  In this study, we examine the impact of LLMs' step-by-step predictions on\ngender bias in unscalable tasks. For this purpose, we construct a benchmark for\nan unscalable task where the LLM is given a list of words comprising feminine,\nmasculine, and gendered occupational words, and is required to count the number\nof feminine and masculine words. In our CoT prompts, we require the LLM to\nexplicitly indicate whether each word in the word list is a feminine or\nmasculine before making the final predictions. With counting and handling the\nmeaning of words, this benchmark has characteristics of both arithmetic\nreasoning and symbolic reasoning. Experimental results in English show that\nwithout step-by-step prediction, most LLMs make socially biased predictions,\ndespite the task being as simple as counting words. Interestingly, CoT\nprompting reduces this unconscious social bias in LLMs and encourages fair\npredictions.",
        "authors": "Masahiro Kaneko, Danushka Bollegala, Naoaki Okazaki, Timothy Baldwin",
        "published": "2024-01-28",
        "updated": "2024-01-28",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2312.14769v3",
        "title": "Large Language Model (LLM) Bias Index -- LLMBI",
        "abstract": "The Large Language Model Bias Index (LLMBI) is a pioneering approach designed\nto quantify and address biases inherent in large language models (LLMs), such\nas GPT-4. We recognise the increasing prevalence and impact of LLMs across\ndiverse sectors. This research introduces a novel metric, LLMBI, to\nsystematically measure and mitigate biases potentially skewing model responses.\nWe formulated LLMBI using a composite scoring system incorporating multiple\ndimensions of bias, including but not limited to age, gender, and racial\nbiases. To operationalise this metric, we engaged in a multi-step process\ninvolving collecting and annotating LLM responses, applying sophisticated\nNatural Language Processing (NLP) techniques for bias detection, and computing\nthe LLMBI score through a specially crafted mathematical formula. The formula\nintegrates weighted averages of various bias dimensions, a penalty for dataset\ndiversity deficiencies, and a correction for sentiment biases. Our empirical\nanalysis, conducted using responses from OpenAI's API, employs advanced\nsentiment analysis as a representative method for bias detection. The research\nreveals LLMs, whilst demonstrating impressive capabilities in text generation,\nexhibit varying degrees of bias across different dimensions. LLMBI provides a\nquantifiable measure to compare biases across models and over time, offering a\nvital tool for systems engineers, researchers and regulators in enhancing the\nfairness and reliability of LLMs. It highlights the potential of LLMs in\nmimicking unbiased human-like responses. Additionally, it underscores the\nnecessity of continuously monitoring and recalibrating such models to align\nwith evolving societal norms and ethical standards.",
        "authors": "Abiodun Finbarrs Oketunji, Muhammad Anas, Deepthi Saina",
        "published": "2023-12-22",
        "updated": "2023-12-29",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI",
            "cs.CY",
            "cs.LG",
            "I.2.7"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2311.04205v2",
        "title": "Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves",
        "abstract": "Misunderstandings arise not only in interpersonal communication but also\nbetween humans and Large Language Models (LLMs). Such discrepancies can make\nLLMs interpret seemingly unambiguous questions in unexpected ways, yielding\nincorrect responses. While it is widely acknowledged that the quality of a\nprompt, such as a question, significantly impacts the quality of the response\nprovided by LLMs, a systematic method for crafting questions that LLMs can\nbetter comprehend is still underdeveloped. In this paper, we present a method\nnamed `Rephrase and Respond' (RaR), which allows LLMs to rephrase and expand\nquestions posed by humans and provide responses in a single prompt. This\napproach serves as a simple yet effective prompting method for improving\nperformance. We also introduce a two-step variant of RaR, where a rephrasing\nLLM first rephrases the question and then passes the original and rephrased\nquestions together to a different responding LLM. This facilitates the\neffective utilization of rephrased questions generated by one LLM with another.\nOur experiments demonstrate that our methods significantly improve the\nperformance of different models across a wide range to tasks. We further\nprovide a comprehensive comparison between RaR and the popular Chain-of-Thought\n(CoT) methods, both theoretically and empirically. We show that RaR is\ncomplementary to CoT and can be combined with CoT to achieve even better\nperformance. Our work not only contributes to enhancing LLM performance\nefficiently and effectively but also sheds light on a fair evaluation of LLM\ncapabilities. Data and codes are available at\nhttps://github.com/uclaml/Rephrase-and-Respond.",
        "authors": "Yihe Deng, Weitong Zhang, Zixiang Chen, Quanquan Gu",
        "published": "2023-11-07",
        "updated": "2024-04-18",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI",
            "cs.LG"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2402.04489v1",
        "title": "De-amplifying Bias from Differential Privacy in Language Model Fine-tuning",
        "abstract": "Fairness and privacy are two important values machine learning (ML)\npractitioners often seek to operationalize in models. Fairness aims to reduce\nmodel bias for social/demographic sub-groups. Privacy via differential privacy\n(DP) mechanisms, on the other hand, limits the impact of any individual's\ntraining data on the resulting model. The trade-offs between privacy and\nfairness goals of trustworthy ML pose a challenge to those wishing to address\nboth. We show that DP amplifies gender, racial, and religious bias when\nfine-tuning large language models (LLMs), producing models more biased than\nones fine-tuned without DP. We find the cause of the amplification to be a\ndisparity in convergence of gradients across sub-groups. Through the case of\nbinary gender bias, we demonstrate that Counterfactual Data Augmentation (CDA),\na known method for addressing bias, also mitigates bias amplification by DP. As\na consequence, DP and CDA together can be used to fine-tune models while\nmaintaining both fairness and privacy.",
        "authors": "Sanjari Srivastava, Piotr Mardziel, Zhikhun Zhang, Archana Ahlawat, Anupam Datta, John C Mitchell",
        "published": "2024-02-07",
        "updated": "2024-02-07",
        "primary_cat": "cs.LG",
        "cats": [
            "cs.LG",
            "cs.CR",
            "cs.CY",
            "stat.ME"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2311.09447v2",
        "title": "How Trustworthy are Open-Source LLMs? An Assessment under Malicious Demonstrations Shows their Vulnerabilities",
        "abstract": "The rapid progress in open-source Large Language Models (LLMs) is\nsignificantly driving AI development forward. However, there is still a limited\nunderstanding of their trustworthiness. Deploying these models at scale without\nsufficient trustworthiness can pose significant risks, highlighting the need to\nuncover these issues promptly. In this work, we conduct an adversarial\nassessment of open-source LLMs on trustworthiness, scrutinizing them across\neight different aspects including toxicity, stereotypes, ethics, hallucination,\nfairness, sycophancy, privacy, and robustness against adversarial\ndemonstrations. We propose advCoU, an extended Chain of Utterances-based (CoU)\nprompting strategy by incorporating carefully crafted malicious demonstrations\nfor trustworthiness attack. Our extensive experiments encompass recent and\nrepresentative series of open-source LLMs, including Vicuna, MPT, Falcon,\nMistral, and Llama 2. The empirical outcomes underscore the efficacy of our\nattack strategy across diverse aspects. More interestingly, our result analysis\nreveals that models with superior performance in general NLP tasks do not\nalways have greater trustworthiness; in fact, larger models can be more\nvulnerable to attacks. Additionally, models that have undergone instruction\ntuning, focusing on instruction following, tend to be more susceptible,\nalthough fine-tuning LLMs for safety alignment proves effective in mitigating\nadversarial trustworthiness attacks.",
        "authors": "Lingbo Mo, Boshi Wang, Muhao Chen, Huan Sun",
        "published": "2023-11-15",
        "updated": "2024-04-02",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2404.02650v1",
        "title": "Towards detecting unanticipated bias in Large Language Models",
        "abstract": "Over the last year, Large Language Models (LLMs) like ChatGPT have become\nwidely available and have exhibited fairness issues similar to those in\nprevious machine learning systems. Current research is primarily focused on\nanalyzing and quantifying these biases in training data and their impact on the\ndecisions of these models, alongside developing mitigation strategies. This\nresearch largely targets well-known biases related to gender, race, ethnicity,\nand language. However, it is clear that LLMs are also affected by other, less\nobvious implicit biases. The complex and often opaque nature of these models\nmakes detecting such biases challenging, yet this is crucial due to their\npotential negative impact in various applications. In this paper, we explore\nnew avenues for detecting these unanticipated biases in LLMs, focusing\nspecifically on Uncertainty Quantification and Explainable AI methods. These\napproaches aim to assess the certainty of model decisions and to make the\ninternal decision-making processes of LLMs more transparent, thereby\nidentifying and understanding biases that are not immediately apparent. Through\nthis research, we aim to contribute to the development of fairer and more\ntransparent AI systems.",
        "authors": "Anna Kruspe",
        "published": "2024-04-03",
        "updated": "2024-04-03",
        "primary_cat": "cs.LG",
        "cats": [
            "cs.LG",
            "cs.AI",
            "cs.CL"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2311.02049v1",
        "title": "Post Turing: Mapping the landscape of LLM Evaluation",
        "abstract": "In the rapidly evolving landscape of Large Language Models (LLMs),\nintroduction of well-defined and standardized evaluation methodologies remains\na crucial challenge. This paper traces the historical trajectory of LLM\nevaluations, from the foundational questions posed by Alan Turing to the modern\nera of AI research. We categorize the evolution of LLMs into distinct periods,\neach characterized by its unique benchmarks and evaluation criteria. As LLMs\nincreasingly mimic human-like behaviors, traditional evaluation proxies, such\nas the Turing test, have become less reliable. We emphasize the pressing need\nfor a unified evaluation system, given the broader societal implications of\nthese models. Through an analysis of common evaluation methodologies, we\nadvocate for a qualitative shift in assessment approaches, underscoring the\nimportance of standardization and objective criteria. This work serves as a\ncall for the AI community to collaboratively address the challenges of LLM\nevaluation, ensuring their reliability, fairness, and societal benefit.",
        "authors": "Alexey Tikhonov, Ivan P. Yamshchikov",
        "published": "2023-11-03",
        "updated": "2023-11-03",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI",
            "68T50",
            "I.2.7"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2311.01964v1",
        "title": "Don't Make Your LLM an Evaluation Benchmark Cheater",
        "abstract": "Large language models~(LLMs) have greatly advanced the frontiers of\nartificial intelligence, attaining remarkable improvement in model capacity. To\nassess the model performance, a typical approach is to construct evaluation\nbenchmarks for measuring the ability level of LLMs in different aspects.\nDespite that a number of high-quality benchmarks have been released, the\nconcerns about the appropriate use of these benchmarks and the fair comparison\nof different models are increasingly growing. Considering these concerns, in\nthis paper, we discuss the potential risk and impact of inappropriately using\nevaluation benchmarks and misleadingly interpreting the evaluation results.\nSpecially, we focus on a special issue that would lead to inappropriate\nevaluation, \\ie \\emph{benchmark leakage}, referring that the data related to\nevaluation sets is occasionally used for model training. This phenomenon now\nbecomes more common since pre-training data is often prepared ahead of model\ntest. We conduct extensive experiments to study the effect of benchmark\nleverage, and find that it can dramatically boost the evaluation results, which\nwould finally lead to an unreliable assessment of model performance. To improve\nthe use of existing evaluation benchmarks, we finally present several\nguidelines for both LLM developers and benchmark maintainers. We hope this work\ncan draw attention to appropriate training and evaluation of LLMs.",
        "authors": "Kun Zhou, Yutao Zhu, Zhipeng Chen, Wentong Chen, Wayne Xin Zhao, Xu Chen, Yankai Lin, Ji-Rong Wen, Jiawei Han",
        "published": "2023-11-03",
        "updated": "2023-11-03",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2404.06003v1",
        "title": "FreeEval: A Modular Framework for Trustworthy and Efficient Evaluation of Large Language Models",
        "abstract": "The rapid development of large language model (LLM) evaluation methodologies\nand datasets has led to a profound challenge: integrating state-of-the-art\nevaluation techniques cost-effectively while ensuring reliability,\nreproducibility, and efficiency. Currently, there is a notable absence of a\nunified and adaptable framework that seamlessly integrates various evaluation\napproaches. Moreover, the reliability of evaluation findings is often\nquestionable due to potential data contamination, with the evaluation\nefficiency commonly overlooked when facing the substantial costs associated\nwith LLM inference. In response to these challenges, we introduce FreeEval, a\nmodular and scalable framework crafted to enable trustworthy and efficient\nautomatic evaluations of LLMs. Firstly, FreeEval's unified abstractions\nsimplify the integration and improve the transparency of diverse evaluation\nmethodologies, encompassing dynamic evaluation that demand sophisticated LLM\ninteractions. Secondly, the framework integrates meta-evaluation techniques\nlike human evaluation and data contamination detection, which, along with\ndynamic evaluation modules in the platform, enhance the fairness of the\nevaluation outcomes. Lastly, FreeEval is designed with a high-performance\ninfrastructure, including distributed computation and caching strategies,\nenabling extensive evaluations across multi-node, multi-GPU clusters for\nopen-source and proprietary LLMs.",
        "authors": "Zhuohao Yu, Chang Gao, Wenjin Yao, Yidong Wang, Zhengran Zeng, Wei Ye, Jindong Wang, Yue Zhang, Shikun Zhang",
        "published": "2024-04-09",
        "updated": "2024-04-09",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2403.00884v2",
        "title": "Text classification of column headers with a controlled vocabulary: leveraging LLMs for metadata enrichment",
        "abstract": "Traditional dataset retrieval systems index on metadata information rather\nthan on the data values. Thus relying primarily on manual annotations and\nhigh-quality metadata, processes known to be labour-intensive and challenging\nto automate. We propose a method to support metadata enrichment with topic\nannotations of column headers using three Large Language Models (LLMs):\nChatGPT-3.5, GoogleBard and GoogleGemini. We investigate the LLMs ability to\nclassify column headers based on domain-specific topics from a controlled\nvocabulary. We evaluate our approach by assessing the internal consistency of\nthe LLMs, the inter-machine alignment, and the human-machine agreement for the\ntopic classification task. Additionally, we investigate the impact of\ncontextual information (i.e. dataset description) on the classification\noutcomes. Our results suggest that ChatGPT and GoogleGemini outperform\nGoogleBard for internal consistency as well as LLM-human-alignment.\nInterestingly, we found that context had no impact on the LLMs performances.\nThis work proposes a novel approach that leverages LLMs for text classification\nusing a controlled topic vocabulary, which has the potential to facilitate\nautomated metadata enrichment, thereby enhancing dataset retrieval and the\nFindability, Accessibility, Interoperability and Reusability (FAIR) of research\ndata on the Web.",
        "authors": "Margherita Martorana, Tobias Kuhn, Lise Stork, Jacco van Ossenbruggen",
        "published": "2024-03-01",
        "updated": "2024-03-05",
        "primary_cat": "cs.DB",
        "cats": [
            "cs.DB",
            "cs.AI",
            "cs.IR"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2401.11033v4",
        "title": "FAIR Enough: How Can We Develop and Assess a FAIR-Compliant Dataset for Large Language Models' Training?",
        "abstract": "The rapid evolution of Large Language Models (LLMs) highlights the necessity\nfor ethical considerations and data integrity in AI development, particularly\nemphasizing the role of FAIR (Findable, Accessible, Interoperable, Reusable)\ndata principles. While these principles are crucial for ethical data\nstewardship, their specific application in the context of LLM training data\nremains an under-explored area. This research gap is the focus of our study,\nwhich begins with an examination of existing literature to underline the\nimportance of FAIR principles in managing data for LLM training. Building upon\nthis, we propose a novel framework designed to integrate FAIR principles into\nthe LLM development lifecycle. A contribution of our work is the development of\na comprehensive checklist intended to guide researchers and developers in\napplying FAIR data principles consistently across the model development\nprocess. The utility and effectiveness of our framework are validated through a\ncase study on creating a FAIR-compliant dataset aimed at detecting and\nmitigating biases in LLMs. We present this framework to the community as a tool\nto foster the creation of technologically advanced, ethically grounded, and\nsocially responsible AI models.",
        "authors": "Shaina Raza, Shardul Ghuge, Chen Ding, Elham Dolatabadi, Deval Pandya",
        "published": "2024-01-19",
        "updated": "2024-04-03",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2305.11595v3",
        "title": "Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate",
        "abstract": "Large Language Models (LLMs) have shown impressive capabilities in various\napplications, but they still face various inconsistency issues. Existing works\nprimarily focus on the inconsistency issues within a single LLM, while we\ncomplementarily explore the inter-consistency among multiple LLMs for\ncollaboration. To examine whether LLMs can collaborate effectively to achieve a\nconsensus for a shared goal, we focus on commonsense reasoning, and introduce a\nformal debate framework (FORD) to conduct a three-stage debate among LLMs with\nreal-world scenarios alignment: fair debate, mismatched debate, and roundtable\ndebate. Through extensive experiments on various datasets, LLMs can effectively\ncollaborate to reach a consensus despite noticeable inter-inconsistencies, but\nimbalances in their abilities can lead to domination by superior LLMs.\nLeveraging a more advanced LLM like GPT-4 as an authoritative judge can boost\ncollaboration performance. Our work contributes to understanding the\ninter-consistency among LLMs and lays the foundation for developing future\ncollaboration methods. Codes and data are available at\nhttps://github.com/Waste-Wood/FORD",
        "authors": "Kai Xiong, Xiao Ding, Yixin Cao, Ting Liu, Bing Qin",
        "published": "2023-05-19",
        "updated": "2023-10-18",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2310.14607v2",
        "title": "Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications",
        "abstract": "Recent literature has suggested the potential of using large language models\n(LLMs) to make classifications for tabular tasks. However, LLMs have been shown\nto exhibit harmful social biases that reflect the stereotypes and inequalities\npresent in society. To this end, as well as the widespread use of tabular data\nin many high-stake applications, it is important to explore the following\nquestions: what sources of information do LLMs draw upon when making\nclassifications for tabular tasks; whether and to what extent are LLM\nclassifications for tabular data influenced by social biases and stereotypes;\nand what are the consequential implications for fairness?\n  Through a series of experiments, we delve into these questions and show that\nLLMs tend to inherit social biases from their training data which significantly\nimpact their fairness in tabular classification tasks. Furthermore, our\ninvestigations show that in the context of bias mitigation, though in-context\nlearning and finetuning have a moderate effect, the fairness metric gap between\ndifferent subgroups is still larger than that in traditional machine learning\nmodels, such as Random Forest and shallow Neural Networks. This observation\nemphasizes that the social biases are inherent within the LLMs themselves and\ninherited from their pretraining corpus, not only from the downstream task\ndatasets. Besides, we demonstrate that label-flipping of in-context examples\ncan significantly reduce biases, further highlighting the presence of inherent\nbias within LLMs.",
        "authors": "Yanchen Liu, Srishti Gautam, Jiaqi Ma, Himabindu Lakkaraju",
        "published": "2023-10-23",
        "updated": "2024-04-02",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.LG"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2311.08472v1",
        "title": "Selecting Shots for Demographic Fairness in Few-Shot Learning with Large Language Models",
        "abstract": "Recently, work in NLP has shifted to few-shot (in-context) learning, with\nlarge language models (LLMs) performing well across a range of tasks. However,\nwhile fairness evaluations have become a standard for supervised methods,\nlittle is known about the fairness of LLMs as prediction systems. Further,\ncommon standard methods for fairness involve access to models weights or are\napplied during finetuning, which are not applicable in few-shot learning. Do\nLLMs exhibit prediction biases when used for standard NLP tasks? In this work,\nwe explore the effect of shots, which directly affect the performance of\nmodels, on the fairness of LLMs as NLP classification systems. We consider how\ndifferent shot selection strategies, both existing and new demographically\nsensitive methods, affect model fairness across three standard fairness\ndatasets. We discuss how future work can include LLM fairness evaluations.",
        "authors": "Carlos Aguirre, Kuleen Sasse, Isabel Cachola, Mark Dredze",
        "published": "2023-11-14",
        "updated": "2023-11-14",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2311.03033v1",
        "title": "Beyond Words: A Mathematical Framework for Interpreting Large Language Models",
        "abstract": "Large language models (LLMs) are powerful AI tools that can generate and\ncomprehend natural language text and other complex information. However, the\nfield lacks a mathematical framework to systematically describe, compare and\nimprove LLMs. We propose Hex a framework that clarifies key terms and concepts\nin LLM research, such as hallucinations, alignment, self-verification and\nchain-of-thought reasoning. The Hex framework offers a precise and consistent\nway to characterize LLMs, identify their strengths and weaknesses, and\nintegrate new findings. Using Hex, we differentiate chain-of-thought reasoning\nfrom chain-of-thought prompting and establish the conditions under which they\nare equivalent. This distinction clarifies the basic assumptions behind\nchain-of-thought prompting and its implications for methods that use it, such\nas self-verification and prompt programming.\n  Our goal is to provide a formal framework for LLMs that can help both\nresearchers and practitioners explore new possibilities for generative AI. We\ndo not claim to have a definitive solution, but rather a tool for opening up\nnew research avenues. We argue that our formal definitions and results are\ncrucial for advancing the discussion on how to build generative AI systems that\nare safe, reliable, fair and robust, especially in domains like healthcare and\nsoftware engineering.",
        "authors": "Javier Gonz\u00e1lez, Aditya V. Nori",
        "published": "2023-11-06",
        "updated": "2023-11-06",
        "primary_cat": "cs.LG",
        "cats": [
            "cs.LG",
            "cs.AI"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2312.15478v1",
        "title": "A Group Fairness Lens for Large Language Models",
        "abstract": "The rapid advancement of large language models has revolutionized various\napplications but also raised crucial concerns about their potential to\nperpetuate biases and unfairness when deployed in social media contexts.\nEvaluating LLMs' potential biases and fairness has become crucial, as existing\nmethods rely on limited prompts focusing on just a few groups, lacking a\ncomprehensive categorical perspective. In this paper, we propose evaluating LLM\nbiases from a group fairness lens using a novel hierarchical schema\ncharacterizing diverse social groups. Specifically, we construct a dataset,\nGFair, encapsulating target-attribute combinations across multiple dimensions.\nIn addition, we introduce statement organization, a new open-ended text\ngeneration task, to uncover complex biases in LLMs. Extensive evaluations of\npopular LLMs reveal inherent safety concerns. To mitigate the biases of LLM\nfrom a group fairness perspective, we pioneer a novel chain-of-thought method\nGF-Think to mitigate biases of LLMs from a group fairness perspective.\nExperimental results demonstrate its efficacy in mitigating bias in LLMs to\nachieve fairness.",
        "authors": "Guanqun Bi, Lei Shen, Yuqiang Xie, Yanan Cao, Tiangang Zhu, Xiaodong He",
        "published": "2023-12-24",
        "updated": "2023-12-24",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2309.09397v1",
        "title": "Do Large GPT Models Discover Moral Dimensions in Language Representations? A Topological Study Of Sentence Embeddings",
        "abstract": "As Large Language Models are deployed within Artificial Intelligence systems,\nthat are increasingly integrated with human society, it becomes more important\nthan ever to study their internal structures. Higher level abilities of LLMs\nsuch as GPT-3.5 emerge in large part due to informative language\nrepresentations they induce from raw text data during pre-training on trillions\nof words. These embeddings exist in vector spaces of several thousand\ndimensions, and their processing involves mapping between multiple vector\nspaces, with total number of parameters on the order of trillions. Furthermore,\nthese language representations are induced by gradient optimization, resulting\nin a black box system that is hard to interpret. In this paper, we take a look\nat the topological structure of neuronal activity in the \"brain\" of Chat-GPT's\nfoundation language model, and analyze it with respect to a metric representing\nthe notion of fairness. We develop a novel approach to visualize GPT's moral\ndimensions. We first compute a fairness metric, inspired by social psychology\nliterature, to identify factors that typically influence fairness assessments\nin humans, such as legitimacy, need, and responsibility. Subsequently, we\nsummarize the manifold's shape using a lower-dimensional simplicial complex,\nwhose topology is derived from this metric. We color it with a heat map\nassociated with this fairness metric, producing human-readable visualizations\nof the high-dimensional sentence manifold. Our results show that sentence\nembeddings based on GPT-3.5 can be decomposed into two submanifolds\ncorresponding to fair and unfair moral judgments. This indicates that GPT-based\nlanguage models develop a moral dimension within their representation spaces\nand induce an understanding of fairness during their training process.",
        "authors": "Stephen Fitz",
        "published": "2023-09-17",
        "updated": "2023-09-17",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI",
            "cs.CY",
            "cs.LG",
            "cs.NE"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2402.06852v2",
        "title": "ChemLLM: A Chemical Large Language Model",
        "abstract": "Large language models (LLMs) have made impressive progress in chemistry\napplications. However, the community lacks an LLM specifically designed for\nchemistry. The main challenges are two-fold: firstly, most chemical data and\nscientific knowledge are stored in structured databases, which limits the\nmodel's ability to sustain coherent dialogue when used directly. Secondly,\nthere is an absence of objective and fair benchmark that encompass most\nchemistry tasks. Here, we introduce ChemLLM, a comprehensive framework that\nfeatures the first LLM dedicated to chemistry. It also includes ChemData, a\ndataset specifically designed for instruction tuning, and ChemBench, a robust\nbenchmark covering nine essential chemistry tasks. ChemLLM is adept at\nperforming various tasks across chemical disciplines with fluid dialogue\ninteraction. Notably, ChemLLM achieves results comparable to GPT-4 on the core\nchemical tasks and demonstrates competitive performance with LLMs of similar\nsize in general scenarios. ChemLLM paves a new path for exploration in chemical\nstudies, and our method of incorporating structured chemical knowledge into\ndialogue systems sets a new standard for developing LLMs in various scientific\nfields. Codes, Datasets, and Model weights are publicly accessible at\nhttps://hf.co/AI4Chem",
        "authors": "Di Zhang, Wei Liu, Qian Tan, Jingdan Chen, Hang Yan, Yuliang Yan, Jiatong Li, Weiran Huang, Xiangyu Yue, Wanli Ouyang, Dongzhan Zhou, Shufei Zhang, Mao Su, Han-Sen Zhong, Yuqiang Li",
        "published": "2024-02-10",
        "updated": "2024-04-25",
        "primary_cat": "cs.AI",
        "cats": [
            "cs.AI",
            "cs.CL"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2403.02839v1",
        "title": "An Empirical Study of LLM-as-a-Judge for LLM Evaluation: Fine-tuned Judge Models are Task-specific Classifiers",
        "abstract": "Recently, there has been a growing trend of utilizing Large Language Model\n(LLM) to evaluate the quality of other LLMs. Many studies have employed\nproprietary close-source models, especially GPT4, as the evaluator.\nAlternatively, other works have fine-tuned judge models based on open-source\nLLMs as the evaluator. In this study, we conduct an empirical study of\ndifferent judge models on their evaluation capability. Our findings indicate\nthat although the fine-tuned judge models achieve high accuracy on in-domain\ntest sets, even surpassing GPT4, they are inherently task-specific classifiers,\nand their generalizability and fairness severely underperform GPT4.",
        "authors": "Hui Huang, Yingqi Qu, Jing Liu, Muyun Yang, Tiejun Zhao",
        "published": "2024-03-05",
        "updated": "2024-03-05",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2311.00306v1",
        "title": "Probing Explicit and Implicit Gender Bias through LLM Conditional Text Generation",
        "abstract": "Large Language Models (LLMs) can generate biased and toxic responses. Yet\nmost prior work on LLM gender bias evaluation requires predefined\ngender-related phrases or gender stereotypes, which are challenging to be\ncomprehensively collected and are limited to explicit bias evaluation. In\naddition, we believe that instances devoid of gender-related language or\nexplicit stereotypes in inputs can still induce gender bias in LLMs. Thus, in\nthis work, we propose a conditional text generation mechanism without the need\nfor predefined gender phrases and stereotypes. This approach employs three\ntypes of inputs generated through three distinct strategies to probe LLMs,\naiming to show evidence of explicit and implicit gender biases in LLMs. We also\nutilize explicit and implicit evaluation metrics to evaluate gender bias in\nLLMs under different strategies. Our experiments demonstrate that an increased\nmodel size does not consistently lead to enhanced fairness and all tested LLMs\nexhibit explicit and/or implicit gender bias, even when explicit gender\nstereotypes are absent in the inputs.",
        "authors": "Xiangjue Dong, Yibo Wang, Philip S. Yu, James Caverlee",
        "published": "2023-11-01",
        "updated": "2023-11-01",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2206.13757v1",
        "title": "Flexible text generation for counterfactual fairness probing",
        "abstract": "A common approach for testing fairness issues in text-based classifiers is\nthrough the use of counterfactuals: does the classifier output change if a\nsensitive attribute in the input is changed? Existing counterfactual generation\nmethods typically rely on wordlists or templates, producing simple\ncounterfactuals that don't take into account grammar, context, or subtle\nsensitive attribute references, and could miss issues that the wordlist\ncreators had not considered. In this paper, we introduce a task for generating\ncounterfactuals that overcomes these shortcomings, and demonstrate how large\nlanguage models (LLMs) can be leveraged to make progress on this task. We show\nthat this LLM-based method can produce complex counterfactuals that existing\nmethods cannot, comparing the performance of various counterfactual generation\nmethods on the Civil Comments dataset and showing their value in evaluating a\ntoxicity classifier.",
        "authors": "Zee Fryer, Vera Axelrod, Ben Packer, Alex Beutel, Jilin Chen, Kellie Webster",
        "published": "2022-06-28",
        "updated": "2022-06-28",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.CY"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2305.01937v1",
        "title": "Can Large Language Models Be an Alternative to Human Evaluations?",
        "abstract": "Human evaluation is indispensable and inevitable for assessing the quality of\ntexts generated by machine learning models or written by humans. However, human\nevaluation is very difficult to reproduce and its quality is notoriously\nunstable, hindering fair comparisons among different natural language\nprocessing (NLP) models and algorithms. Recently, large language models (LLMs)\nhave demonstrated exceptional performance on unseen tasks when only the task\ninstructions are provided. In this paper, we explore if such an ability of the\nLLMs can be used as an alternative to human evaluation. We present the LLMs\nwith the exact same instructions, samples to be evaluated, and questions used\nto conduct human evaluation, and then ask the LLMs to generate responses to\nthose questions; we dub this LLM evaluation. We use human evaluation and LLM\nevaluation to evaluate the texts in two NLP tasks: open-ended story generation\nand adversarial attacks. We show that the result of LLM evaluation is\nconsistent with the results obtained by expert human evaluation: the texts\nrated higher by human experts are also rated higher by the LLMs. We also find\nthat the results of LLM evaluation are stable over different formatting of the\ntask instructions and the sampling algorithm used to generate the answer. We\nare the first to show the potential of using LLMs to assess the quality of\ntexts and discuss the limitations and ethical considerations of LLM evaluation.",
        "authors": "Cheng-Han Chiang, Hung-yi Lee",
        "published": "2023-05-03",
        "updated": "2023-05-03",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.HC"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2309.11653v2",
        "title": "\"It's a Fair Game\", or Is It? Examining How Users Navigate Disclosure Risks and Benefits When Using LLM-Based Conversational Agents",
        "abstract": "The widespread use of Large Language Model (LLM)-based conversational agents\n(CAs), especially in high-stakes domains, raises many privacy concerns.\nBuilding ethical LLM-based CAs that respect user privacy requires an in-depth\nunderstanding of the privacy risks that concern users the most. However,\nexisting research, primarily model-centered, does not provide insight into\nusers' perspectives. To bridge this gap, we analyzed sensitive disclosures in\nreal-world ChatGPT conversations and conducted semi-structured interviews with\n19 LLM-based CA users. We found that users are constantly faced with trade-offs\nbetween privacy, utility, and convenience when using LLM-based CAs. However,\nusers' erroneous mental models and the dark patterns in system design limited\ntheir awareness and comprehension of the privacy risks. Additionally, the\nhuman-like interactions encouraged more sensitive disclosures, which\ncomplicated users' ability to navigate the trade-offs. We discuss practical\ndesign guidelines and the needs for paradigm shifts to protect the privacy of\nLLM-based CA users.",
        "authors": "Zhiping Zhang, Michelle Jia, Hao-Ping Lee, Bingsheng Yao, Sauvik Das, Ada Lerner, Dakuo Wang, Tianshi Li",
        "published": "2023-09-20",
        "updated": "2024-04-02",
        "primary_cat": "cs.HC",
        "cats": [
            "cs.HC",
            "cs.AI",
            "cs.CR"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2402.18502v1",
        "title": "Few-Shot Fairness: Unveiling LLM's Potential for Fairness-Aware Classification",
        "abstract": "Employing Large Language Models (LLM) in various downstream applications such\nas classification is crucial, especially for smaller companies lacking the\nexpertise and resources required for fine-tuning a model. Fairness in LLMs\nhelps ensure inclusivity, equal representation based on factors such as race,\ngender and promotes responsible AI deployment. As the use of LLMs has become\nincreasingly prevalent, it is essential to assess whether LLMs can generate\nfair outcomes when subjected to considerations of fairness. In this study, we\nintroduce a framework outlining fairness regulations aligned with various\nfairness definitions, with each definition being modulated by varying degrees\nof abstraction. We explore the configuration for in-context learning and the\nprocedure for selecting in-context demonstrations using RAG, while\nincorporating fairness rules into the process. Experiments conducted with\ndifferent LLMs indicate that GPT-4 delivers superior results in terms of both\naccuracy and fairness compared to other models. This work is one of the early\nattempts to achieve fairness in prediction tasks by utilizing LLMs through\nin-context learning.",
        "authors": "Garima Chhikara, Anurag Sharma, Kripabandhu Ghosh, Abhijnan Chakraborty",
        "published": "2024-02-28",
        "updated": "2024-02-28",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2305.12090v1",
        "title": "UP5: Unbiased Foundation Model for Fairness-aware Recommendation",
        "abstract": "Recent advancements in foundation models such as large language models (LLM)\nhave propelled them to the forefront of recommender systems (RS). Moreover,\nfairness in RS is critical since many users apply it for decision-making and\ndemand fulfillment. However, at present, there is a lack of understanding\nregarding the level of fairness exhibited by recommendation foundation models\nand the appropriate methods for equitably treating different groups of users in\nfoundation models. In this paper, we focus on user-side unfairness problem and\nshow through a thorough examination that there is unfairness involved in LLMs\nthat lead to unfair recommendation results. To eliminate bias from LLM for\nfairness-aware recommendation, we introduce a novel Unbiased P5 (UP5)\nfoundation model based on Counterfactually-Fair-Prompting (CFP) techniques. CFP\nincludes two sub-modules: a personalized prefix prompt that enhances fairness\nwith respect to individual sensitive attributes, and a Prompt Mixture that\nintegrates multiple counterfactually-fair prompts for a set of sensitive\nattributes. Experiments are conducted on two real-world datasets, MovieLens-1M\nand Insurance, and results are compared with both matching-based and\nsequential-based fairness-aware recommendation models. The results show that\nUP5 achieves better recommendation performance and meanwhile exhibits a high\nlevel of fairness.",
        "authors": "Wenyue Hua, Yingqiang Ge, Shuyuan Xu, Jianchao Ji, Yongfeng Zhang",
        "published": "2023-05-20",
        "updated": "2023-05-20",
        "primary_cat": "cs.IR",
        "cats": [
            "cs.IR",
            "cs.AI",
            "cs.CL",
            "cs.LG"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2305.03514v3",
        "title": "Can Large Language Models Transform Computational Social Science?",
        "abstract": "Large Language Models (LLMs) are capable of successfully performing many\nlanguage processing tasks zero-shot (without training data). If zero-shot LLMs\ncan also reliably classify and explain social phenomena like persuasiveness and\npolitical ideology, then LLMs could augment the Computational Social Science\n(CSS) pipeline in important ways. This work provides a road map for using LLMs\nas CSS tools. Towards this end, we contribute a set of prompting best practices\nand an extensive evaluation pipeline to measure the zero-shot performance of 13\nlanguage models on 25 representative English CSS benchmarks. On taxonomic\nlabeling tasks (classification), LLMs fail to outperform the best fine-tuned\nmodels but still achieve fair levels of agreement with humans. On free-form\ncoding tasks (generation), LLMs produce explanations that often exceed the\nquality of crowdworkers' gold references. We conclude that the performance of\ntoday's LLMs can augment the CSS research pipeline in two ways: (1) serving as\nzero-shot data annotators on human annotation teams, and (2) bootstrapping\nchallenging creative generation tasks (e.g., explaining the underlying\nattributes of a text). In summary, LLMs are posed to meaningfully participate\nin social science analysis in partnership with humans.",
        "authors": "Caleb Ziems, William Held, Omar Shaikh, Jiaao Chen, Zhehao Zhang, Diyi Yang",
        "published": "2023-04-12",
        "updated": "2024-02-26",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.LG"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2311.07884v2",
        "title": "Fair Abstractive Summarization of Diverse Perspectives",
        "abstract": "People from different social and demographic groups express diverse\nperspectives and conflicting opinions on a broad set of topics such as product\nreviews, healthcare, law, and politics. A fair summary should provide a\ncomprehensive coverage of diverse perspectives without underrepresenting\ncertain groups. However, current work in summarization metrics and Large\nLanguage Models (LLMs) evaluation has not explored fair abstractive\nsummarization. In this paper, we systematically investigate fair abstractive\nsummarization for user-generated data. We first formally define fairness in\nabstractive summarization as not underrepresenting perspectives of any groups\nof people, and we propose four reference-free automatic metrics by measuring\nthe differences between target and source perspectives. We evaluate nine LLMs,\nincluding three GPT models, four LLaMA models, PaLM 2, and Claude, on six\ndatasets collected from social media, online reviews, and recorded transcripts.\nExperiments show that both the model-generated and the human-written reference\nsummaries suffer from low fairness. We conduct a comprehensive analysis of the\ncommon factors influencing fairness and propose three simple but effective\nmethods to alleviate unfair summarization. Our dataset and code are available\nat https://github.com/psunlpgroup/FairSumm.",
        "authors": "Yusen Zhang, Nan Zhang, Yixin Liu, Alexander Fabbri, Junru Liu, Ryo Kamoi, Xiaoxin Lu, Caiming Xiong, Jieyu Zhao, Dragomir Radev, Kathleen McKeown, Rui Zhang",
        "published": "2023-11-14",
        "updated": "2024-03-30",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2311.02294v1",
        "title": "LLMs grasp morality in concept",
        "abstract": "Work in AI ethics and fairness has made much progress in regulating LLMs to\nreflect certain values, such as fairness, truth, and diversity. However, it has\ntaken the problem of how LLMs might 'mean' anything at all for granted. Without\naddressing this, it is not clear what imbuing LLMs with such values even means.\nIn response, we provide a general theory of meaning that extends beyond humans.\nWe use this theory to explicate the precise nature of LLMs as meaning-agents.\nWe suggest that the LLM, by virtue of its position as a meaning-agent, already\ngrasps the constructions of human society (e.g. morality, gender, and race) in\nconcept. Consequently, under certain ethical frameworks, currently popular\nmethods for model alignment are limited at best and counterproductive at worst.\nMoreover, unaligned models may help us better develop our moral and social\nphilosophy.",
        "authors": "Mark Pock, Andre Ye, Jared Moore",
        "published": "2023-11-04",
        "updated": "2023-11-04",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.CY"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2312.14804v1",
        "title": "Use large language models to promote equity",
        "abstract": "Advances in large language models (LLMs) have driven an explosion of interest\nabout their societal impacts. Much of the discourse around how they will impact\nsocial equity has been cautionary or negative, focusing on questions like \"how\nmight LLMs be biased and how would we mitigate those biases?\" This is a vital\ndiscussion: the ways in which AI generally, and LLMs specifically, can entrench\nbiases have been well-documented. But equally vital, and much less discussed,\nis the more opportunity-focused counterpoint: \"what promising applications do\nLLMs enable that could promote equity?\" If LLMs are to enable a more equitable\nworld, it is not enough just to play defense against their biases and failure\nmodes. We must also go on offense, applying them positively to equity-enhancing\nuse cases to increase opportunities for underserved groups and reduce societal\ndiscrimination. There are many choices which determine the impact of AI, and a\nfundamental choice very early in the pipeline is the problems we choose to\napply it to. If we focus only later in the pipeline -- making LLMs marginally\nmore fair as they facilitate use cases which intrinsically entrench power -- we\nwill miss an important opportunity to guide them to equitable impacts. Here, we\nhighlight the emerging potential of LLMs to promote equity by presenting four\nnewly possible, promising research directions, while keeping risks and\ncautionary points in clear view.",
        "authors": "Emma Pierson, Divya Shanmugam, Rajiv Movva, Jon Kleinberg, Monica Agrawal, Mark Dredze, Kadija Ferryman, Judy Wawira Gichoya, Dan Jurafsky, Pang Wei Koh, Karen Levy, Sendhil Mullainathan, Ziad Obermeyer, Harini Suresh, Keyon Vafa",
        "published": "2023-12-22",
        "updated": "2023-12-22",
        "primary_cat": "cs.CY",
        "cats": [
            "cs.CY"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2307.15997v1",
        "title": "RoCar: A Relationship Network-based Evaluation Method to Large Language Models",
        "abstract": "Large language models (LLMs) have received increasing attention. However, due\nto the complexity of its capabilities, how to rationally evaluate the\ncapabilities of LLMs is still a task to be solved. We propose the RoCar method,\nwhich utilizes the defined basic schemas to randomly construct a task graph and\ngenerates natural language evaluation tasks based on the task graph to evaluate\nthe reasoning and memory abilities of LLMs respectively. Due to the very large\nrandomness of the task construction process, it is possible to ensure that none\nof the LLMs to be tested has directly learned the evaluation tasks,\nguaranteeing the fairness of the evaluation method.",
        "authors": "Ming Wang, Wenfang Wu, Chongyun Gao, Daling Wang, Shi Feng, Yifei Zhang",
        "published": "2023-07-29",
        "updated": "2023-07-29",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2307.11761v1",
        "title": "Fairness of ChatGPT and the Role Of Explainable-Guided Prompts",
        "abstract": "Our research investigates the potential of Large-scale Language Models\n(LLMs), specifically OpenAI's GPT, in credit risk assessment-a binary\nclassification task. Our findings suggest that LLMs, when directed by\njudiciously designed prompts and supplemented with domain-specific knowledge,\ncan parallel the performance of traditional Machine Learning (ML) models.\nIntriguingly, they achieve this with significantly less data-40 times less,\nutilizing merely 20 data points compared to the ML's 800. LLMs particularly\nexcel in minimizing false positives and enhancing fairness, both being vital\naspects of risk analysis. While our results did not surpass those of classical\nML models, they underscore the potential of LLMs in analogous tasks, laying a\ngroundwork for future explorations into harnessing the capabilities of LLMs in\ndiverse ML tasks.",
        "authors": "Yashar Deldjoo",
        "published": "2023-07-14",
        "updated": "2023-07-14",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2403.00811v1",
        "title": "Cognitive Bias in High-Stakes Decision-Making with LLMs",
        "abstract": "Large language models (LLMs) offer significant potential as tools to support\nan expanding range of decision-making tasks. However, given their training on\nhuman (created) data, LLMs can inherit both societal biases against protected\ngroups, as well as be subject to cognitive bias. Such human-like bias can\nimpede fair and explainable decisions made with LLM assistance. Our work\nintroduces BiasBuster, a framework designed to uncover, evaluate, and mitigate\ncognitive bias in LLMs, particularly in high-stakes decision-making tasks.\nInspired by prior research in psychology and cognitive sciences, we develop a\ndataset containing 16,800 prompts to evaluate different cognitive biases (e.g.,\nprompt-induced, sequential, inherent). We test various bias mitigation\nstrategies, amidst proposing a novel method using LLMs to debias their own\nprompts. Our analysis provides a comprehensive picture on the presence and\neffects of cognitive bias across different commercial and open-source models.\nWe demonstrate that our self-help debiasing effectively mitigate cognitive bias\nwithout having to manually craft examples for each bias type.",
        "authors": "Jessica Echterhoff, Yao Liu, Abeer Alessa, Julian McAuley, Zexue He",
        "published": "2024-02-25",
        "updated": "2024-02-25",
        "primary_cat": "cs.AI",
        "cats": [
            "cs.AI",
            "cs.CL"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2309.08836v2",
        "title": "Bias and Fairness in Chatbots: An Overview",
        "abstract": "Chatbots have been studied for more than half a century. With the rapid\ndevelopment of natural language processing (NLP) technologies in recent years,\nchatbots using large language models (LLMs) have received much attention\nnowadays. Compared with traditional ones, modern chatbots are more powerful and\nhave been used in real-world applications. There are however, bias and fairness\nconcerns in modern chatbot design. Due to the huge amounts of training data,\nextremely large model sizes, and lack of interpretability, bias mitigation and\nfairness preservation of modern chatbots are challenging. Thus, a comprehensive\noverview on bias and fairness in chatbot systems is given in this paper. The\nhistory of chatbots and their categories are first reviewed. Then, bias sources\nand potential harms in applications are analyzed. Considerations in designing\nfair and unbiased chatbot systems are examined. Finally, future research\ndirections are discussed.",
        "authors": "Jintang Xue, Yun-Cheng Wang, Chengwei Wei, Xiaofeng Liu, Jonghye Woo, C. -C. Jay Kuo",
        "published": "2023-09-16",
        "updated": "2023-12-10",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI",
            "cs.CY"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2305.18569v1",
        "title": "Fairness of ChatGPT",
        "abstract": "Understanding and addressing unfairness in LLMs are crucial for responsible\nAI deployment. However, there is a limited availability of quantitative\nanalyses and in-depth studies regarding fairness evaluations in LLMs,\nespecially when applying LLMs to high-stakes fields. This work aims to fill\nthis gap by providing a systematic evaluation of the effectiveness and fairness\nof LLMs using ChatGPT as a study case. We focus on assessing ChatGPT's\nperformance in high-takes fields including education, criminology, finance and\nhealthcare. To make thorough evaluation, we consider both group fairness and\nindividual fairness and we also observe the disparities in ChatGPT's outputs\nunder a set of biased or unbiased prompts. This work contributes to a deeper\nunderstanding of LLMs' fairness performance, facilitates bias mitigation and\nfosters the development of responsible artificial intelligence systems.",
        "authors": "Yunqi Li, Yongfeng Zhang",
        "published": "2023-05-22",
        "updated": "2023-05-22",
        "primary_cat": "cs.LG",
        "cats": [
            "cs.LG",
            "cs.AI",
            "cs.CL",
            "cs.CY"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2403.04814v2",
        "title": "Evaluation of LLMs on Syntax-Aware Code Fill-in-the-Middle Tasks",
        "abstract": "We introduce Syntax-Aware Fill-In-the-Middle (SAFIM), a new benchmark for\nevaluating Large Language Models (LLMs) on the code Fill-in-the-Middle (FIM)\ntask. This benchmark focuses on syntax-aware completions of program structures\nsuch as code blocks and conditional expressions, and includes 17,720 examples\nfrom multiple programming languages, sourced from recent code submissions after\nApril 2022 to minimize data contamination. SAFIM provides a robust framework\nwith various prompt designs and novel syntax-aware post-processing techniques,\nfacilitating accurate and fair comparisons across LLMs. Our comprehensive\nevaluation of 15 LLMs shows that FIM pretraining not only enhances FIM\nproficiency but also improves Left-to-Right (L2R) inference using LLMs. Our\nfindings challenge conventional beliefs and suggest that pretraining methods\nand data quality have more impact than model size. SAFIM thus serves as a\nfoundational platform for future research in effective pretraining strategies\nfor code LLMs. The evaluation toolkit and dataset are available at\nhttps://github.com/gonglinyuan/safim, and the leaderboard is available at\nhttps://safimbenchmark.com.",
        "authors": "Linyuan Gong, Sida Wang, Mostafa Elhoushi, Alvin Cheung",
        "published": "2024-03-07",
        "updated": "2024-04-10",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI",
            "cs.LG",
            "cs.SE"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2310.15007v1",
        "title": "Did the Neurons Read your Book? Document-level Membership Inference for Large Language Models",
        "abstract": "With large language models (LLMs) poised to become embedded in our daily\nlives, questions are starting to be raised about the dataset(s) they learned\nfrom. These questions range from potential bias or misinformation LLMs could\nretain from their training data to questions of copyright and fair use of\nhuman-generated text. However, while these questions emerge, developers of the\nrecent state-of-the-art LLMs become increasingly reluctant to disclose details\non their training corpus. We here introduce the task of document-level\nmembership inference for real-world LLMs, i.e. inferring whether the LLM has\nseen a given document during training or not. First, we propose a procedure for\nthe development and evaluation of document-level membership inference for LLMs\nby leveraging commonly used data sources for training and the model release\ndate. We then propose a practical, black-box method to predict document-level\nmembership and instantiate it on OpenLLaMA-7B with both books and academic\npapers. We show our methodology to perform very well, reaching an impressive\nAUC of 0.856 for books and 0.678 for papers. We then show our approach to\noutperform the sentence-level membership inference attacks used in the privacy\nliterature for the document-level membership task. We finally evaluate whether\nsmaller models might be less sensitive to document-level inference and show\nOpenLLaMA-3B to be approximately as sensitive as OpenLLaMA-7B to our approach.\nTaken together, our results show that accurate document-level membership can be\ninferred for LLMs, increasing the transparency of technology poised to change\nour lives.",
        "authors": "Matthieu Meeus, Shubham Jain, Marek Rei, Yves-Alexandre de Montjoye",
        "published": "2023-10-23",
        "updated": "2023-10-23",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.CR",
            "cs.LG"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2402.15215v1",
        "title": "Item-side Fairness of Large Language Model-based Recommendation System",
        "abstract": "Recommendation systems for Web content distribution intricately connect to\nthe information access and exposure opportunities for vulnerable populations.\nThe emergence of Large Language Models-based Recommendation System (LRS) may\nintroduce additional societal challenges to recommendation systems due to the\ninherent biases in Large Language Models (LLMs). From the perspective of\nitem-side fairness, there remains a lack of comprehensive investigation into\nthe item-side fairness of LRS given the unique characteristics of LRS compared\nto conventional recommendation systems. To bridge this gap, this study examines\nthe property of LRS with respect to item-side fairness and reveals the\ninfluencing factors of both historical users' interactions and inherent\nsemantic biases of LLMs, shedding light on the need to extend conventional\nitem-side fairness methods for LRS. Towards this goal, we develop a concise and\neffective framework called IFairLRS to enhance the item-side fairness of an\nLRS. IFairLRS covers the main stages of building an LRS with specifically\nadapted strategies to calibrate the recommendations of LRS. We utilize IFairLRS\nto fine-tune LLaMA, a representative LLM, on \\textit{MovieLens} and\n\\textit{Steam} datasets, and observe significant item-side fairness\nimprovements. The code can be found in\nhttps://github.com/JiangM-C/IFairLRS.git.",
        "authors": "Meng Jiang, Keqin Bao, Jizhi Zhang, Wenjie Wang, Zhengyi Yang, Fuli Feng, Xiangnan He",
        "published": "2024-02-23",
        "updated": "2024-02-23",
        "primary_cat": "cs.IR",
        "cats": [
            "cs.IR"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2403.05668v1",
        "title": "CFaiRLLM: Consumer Fairness Evaluation in Large-Language Model Recommender System",
        "abstract": "In the evolving landscape of recommender systems, the integration of Large\nLanguage Models (LLMs) such as ChatGPT marks a new era, introducing the concept\nof Recommendation via LLM (RecLLM). While these advancements promise\nunprecedented personalization and efficiency, they also bring to the fore\ncritical concerns regarding fairness, particularly in how recommendations might\ninadvertently perpetuate or amplify biases associated with sensitive user\nattributes. In order to address these concerns, our study introduces a\ncomprehensive evaluation framework, CFaiRLLM, aimed at evaluating (and thereby\nmitigating) biases on the consumer side within RecLLMs.\n  Our research methodically assesses the fairness of RecLLMs by examining how\nrecommendations might vary with the inclusion of sensitive attributes such as\ngender, age, and their intersections, through both similarity alignment and\ntrue preference alignment. By analyzing recommendations generated under\ndifferent conditions-including the use of sensitive attributes in user\nprompts-our framework identifies potential biases in the recommendations\nprovided. A key part of our study involves exploring how different detailed\nstrategies for constructing user profiles (random, top-rated, recent) impact\nthe alignment between recommendations made without consideration of sensitive\nattributes and those that are sensitive-attribute-aware, highlighting the bias\nmechanisms within RecLLMs.\n  The findings in our study highlight notable disparities in the fairness of\nrecommendations, particularly when sensitive attributes are integrated into the\nrecommendation process, either individually or in combination. The analysis\ndemonstrates that the choice of user profile sampling strategy plays a\nsignificant role in affecting fairness outcomes, highlighting the complexity of\nachieving fair recommendations in the era of LLMs.",
        "authors": "Yashar Deldjoo, Tommaso di Noia",
        "published": "2024-03-08",
        "updated": "2024-03-08",
        "primary_cat": "cs.IR",
        "cats": [
            "cs.IR"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2312.15198v2",
        "title": "Do LLM Agents Exhibit Social Behavior?",
        "abstract": "The advances of Large Language Models (LLMs) are expanding their utility in\nboth academic research and practical applications. Recent social science\nresearch has explored the use of these ``black-box'' LLM agents for simulating\ncomplex social systems and potentially substituting human subjects in\nexperiments. Our study delves into this emerging domain, investigating the\nextent to which LLMs exhibit key social interaction principles, such as social\nlearning, social preference, and cooperative behavior (indirect reciprocity),\nin their interactions with humans and other agents. We develop a framework for\nour study, wherein classical laboratory experiments involving human subjects\nare adapted to use LLM agents. This approach involves step-by-step reasoning\nthat mirrors human cognitive processes and zero-shot learning to assess the\ninnate preferences of LLMs. Our analysis of LLM agents' behavior includes both\nthe primary effects and an in-depth examination of the underlying mechanisms.\nFocusing on GPT-4, our analyses suggest that LLM agents appear to exhibit a\nrange of human-like social behaviors such as distributional and reciprocity\npreferences, responsiveness to group identity cues, engagement in indirect\nreciprocity, and social learning capabilities. However, our analysis also\nreveals notable differences: LLMs demonstrate a pronounced fairness preference,\nweaker positive reciprocity, and a more calculating approach in social learning\ncompared to humans. These insights indicate that while LLMs hold great promise\nfor applications in social science research, such as in laboratory experiments\nand agent-based modeling, the subtle behavioral differences between LLM agents\nand humans warrant further investigation. Careful examination and development\nof protocols in evaluating the social behaviors of LLMs are necessary before\ndirectly applying these models to emulate human behavior.",
        "authors": "Yan Leng, Yuan Yuan",
        "published": "2023-12-23",
        "updated": "2024-02-22",
        "primary_cat": "cs.AI",
        "cats": [
            "cs.AI",
            "cs.SI",
            "econ.GN",
            "q-fin.EC"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2402.07688v1",
        "title": "CyberMetric: A Benchmark Dataset for Evaluating Large Language Models Knowledge in Cybersecurity",
        "abstract": "Large Language Models (LLMs) excel across various domains, from computer\nvision to medical diagnostics. However, understanding the diverse landscape of\ncybersecurity, encompassing cryptography, reverse engineering, and managerial\nfacets like risk assessment, presents a challenge, even for human experts. In\nthis paper, we introduce CyberMetric, a benchmark dataset comprising 10,000\nquestions sourced from standards, certifications, research papers, books, and\nother publications in the cybersecurity domain. The questions are created\nthrough a collaborative process, i.e., merging expert knowledge with LLMs,\nincluding GPT-3.5 and Falcon-180B. Human experts spent over 200 hours verifying\ntheir accuracy and relevance. Beyond assessing LLMs' knowledge, the dataset's\nmain goal is to facilitate a fair comparison between humans and different LLMs\nin cybersecurity. To achieve this, we carefully selected 80 questions covering\na wide range of topics within cybersecurity and involved 30 participants of\ndiverse expertise levels, facilitating a comprehensive comparison between human\nand machine intelligence in this area. The findings revealed that LLMs\noutperformed humans in almost every aspect of cybersecurity.",
        "authors": "Norbert Tihanyi, Mohamed Amine Ferrag, Ridhi Jain, Merouane Debbah",
        "published": "2024-02-12",
        "updated": "2024-02-12",
        "primary_cat": "cs.AI",
        "cats": [
            "cs.AI",
            "cs.CR"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2402.14208v2",
        "title": "Content Conditional Debiasing for Fair Text Embedding",
        "abstract": "Mitigating biases in machine learning models has gained increasing attention\nin Natural Language Processing (NLP). Yet, only a few studies focus on fair\ntext embeddings, which are crucial yet challenging for real-world applications.\nIn this paper, we propose a novel method for learning fair text embeddings. We\nachieve fairness while maintaining utility trade-off by ensuring conditional\nindependence between sensitive attributes and text embeddings conditioned on\nthe content. Specifically, we enforce that embeddings of texts with different\nsensitive attributes but identical content maintain the same distance toward\nthe embedding of their corresponding neutral text. Furthermore, we address the\nissue of lacking proper training data by using Large Language Models (LLMs) to\naugment texts into different sensitive groups. Our extensive evaluations\ndemonstrate that our approach effectively improves fairness while preserving\nthe utility of embeddings, representing a pioneering effort in achieving\nconditional independence for fair text embeddings.",
        "authors": "Wenlong Deng, Blair Chen, Xiaoxiao Li, Christos Thrampoulidis",
        "published": "2024-02-22",
        "updated": "2024-02-23",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI",
            "cs.CY",
            "cs.LG"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2404.08656v1",
        "title": "Linear Cross-document Event Coreference Resolution with X-AMR",
        "abstract": "Event Coreference Resolution (ECR) as a pairwise mention classification task\nis expensive both for automated systems and manual annotations. The task's\nquadratic difficulty is exacerbated when using Large Language Models (LLMs),\nmaking prompt engineering for ECR prohibitively costly. In this work, we\npropose a graphical representation of events, X-AMR, anchored around individual\nmentions using a \\textbf{cross}-document version of \\textbf{A}bstract\n\\textbf{M}eaning \\textbf{R}epresentation. We then linearize the ECR with a\nnovel multi-hop coreference algorithm over the event graphs. The event graphs\nsimplify ECR, making it a) LLM cost-effective, b) compositional and\ninterpretable, and c) easily annotated. For a fair assessment, we first enrich\nan existing ECR benchmark dataset with these event graphs using an\nannotator-friendly tool we introduce. Then, we employ GPT-4, the newest LLM by\nOpenAI, for these annotations. Finally, using the ECR algorithm, we assess\nGPT-4 against humans and analyze its limitations. Through this research, we aim\nto advance the state-of-the-art for efficient ECR and shed light on the\npotential shortcomings of current LLMs at this task. Code and annotations:\n\\url{https://github.com/ahmeshaf/gpt_coref}",
        "authors": "Shafiuddin Rehan Ahmed, George Arthur Baker, Evi Judge, Michael Regan, Kristin Wright-Bettner, Martha Palmer, James H. Martin",
        "published": "2024-03-25",
        "updated": "2024-03-25",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2311.06899v4",
        "title": "Flames: Benchmarking Value Alignment of LLMs in Chinese",
        "abstract": "The widespread adoption of large language models (LLMs) across various\nregions underscores the urgent need to evaluate their alignment with human\nvalues. Current benchmarks, however, fall short of effectively uncovering\nsafety vulnerabilities in LLMs. Despite numerous models achieving high scores\nand 'topping the chart' in these evaluations, there is still a significant gap\nin LLMs' deeper alignment with human values and achieving genuine harmlessness.\nTo this end, this paper proposes a value alignment benchmark named Flames,\nwhich encompasses both common harmlessness principles and a unique morality\ndimension that integrates specific Chinese values such as harmony. Accordingly,\nwe carefully design adversarial prompts that incorporate complex scenarios and\njailbreaking methods, mostly with implicit malice. By prompting 17 mainstream\nLLMs, we obtain model responses and rigorously annotate them for detailed\nevaluation. Our findings indicate that all the evaluated LLMs demonstrate\nrelatively poor performance on Flames, particularly in the safety and fairness\ndimensions. We also develop a lightweight specified scorer capable of scoring\nLLMs across multiple dimensions to efficiently evaluate new models on the\nbenchmark. The complexity of Flames has far exceeded existing benchmarks,\nsetting a new challenge for contemporary LLMs and highlighting the need for\nfurther alignment of LLMs. Our benchmark is publicly available at\nhttps://github.com/AIFlames/Flames.",
        "authors": "Kexin Huang, Xiangyang Liu, Qianyu Guo, Tianxiang Sun, Jiawei Sun, Yaru Wang, Zeyang Zhou, Yixu Wang, Yan Teng, Xipeng Qiu, Yingchun Wang, Dahua Lin",
        "published": "2023-11-12",
        "updated": "2024-04-15",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2307.03838v2",
        "title": "RADAR: Robust AI-Text Detection via Adversarial Learning",
        "abstract": "Recent advances in large language models (LLMs) and the intensifying\npopularity of ChatGPT-like applications have blurred the boundary of\nhigh-quality text generation between humans and machines. However, in addition\nto the anticipated revolutionary changes to our technology and society, the\ndifficulty of distinguishing LLM-generated texts (AI-text) from human-generated\ntexts poses new challenges of misuse and fairness, such as fake content\ngeneration, plagiarism, and false accusations of innocent writers. While\nexisting works show that current AI-text detectors are not robust to LLM-based\nparaphrasing, this paper aims to bridge this gap by proposing a new framework\ncalled RADAR, which jointly trains a robust AI-text detector via adversarial\nlearning. RADAR is based on adversarial training of a paraphraser and a\ndetector. The paraphraser's goal is to generate realistic content to evade\nAI-text detection. RADAR uses the feedback from the detector to update the\nparaphraser, and vice versa. Evaluated with 8 different LLMs (Pythia, Dolly\n2.0, Palmyra, Camel, GPT-J, Dolly 1.0, LLaMA, and Vicuna) across 4 datasets,\nexperimental results show that RADAR significantly outperforms existing AI-text\ndetection methods, especially when paraphrasing is in place. We also identify\nthe strong transferability of RADAR from instruction-tuned LLMs to other LLMs,\nand evaluate the improved capability of RADAR via GPT-3.5-Turbo.",
        "authors": "Xiaomeng Hu, Pin-Yu Chen, Tsung-Yi Ho",
        "published": "2023-07-07",
        "updated": "2023-10-24",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI",
            "cs.LG"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2402.19465v1",
        "title": "Towards Tracing Trustworthiness Dynamics: Revisiting Pre-training Period of Large Language Models",
        "abstract": "Ensuring the trustworthiness of large language models (LLMs) is crucial. Most\nstudies concentrate on fully pre-trained LLMs to better understand and improve\nLLMs' trustworthiness. In this paper, to reveal the untapped potential of\npre-training, we pioneer the exploration of LLMs' trustworthiness during this\nperiod, focusing on five key dimensions: reliability, privacy, toxicity,\nfairness, and robustness. To begin with, we apply linear probing to LLMs. The\nhigh probing accuracy suggests that \\textit{LLMs in early pre-training can\nalready distinguish concepts in each trustworthiness dimension}. Therefore, to\nfurther uncover the hidden possibilities of pre-training, we extract steering\nvectors from a LLM's pre-training checkpoints to enhance the LLM's\ntrustworthiness. Finally, inspired by~\\citet{choi2023understanding} that mutual\ninformation estimation is bounded by linear probing accuracy, we also probe\nLLMs with mutual information to investigate the dynamics of trustworthiness\nduring pre-training. We are the first to observe a similar two-phase\nphenomenon: fitting and compression~\\citep{shwartz2017opening}. This research\nprovides an initial exploration of trustworthiness modeling during LLM\npre-training, seeking to unveil new insights and spur further developments in\nthe field. We will make our code publicly accessible at\n\\url{https://github.com/ChnQ/TracingLLM}.",
        "authors": "Chen Qian, Jie Zhang, Wei Yao, Dongrui Liu, Zhenfei Yin, Yu Qiao, Yong Liu, Jing Shao",
        "published": "2024-02-29",
        "updated": "2024-02-29",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2310.13343v1",
        "title": "Challenges and Contributing Factors in the Utilization of Large Language Models (LLMs)",
        "abstract": "With the development of large language models (LLMs) like the GPT series,\ntheir widespread use across various application scenarios presents a myriad of\nchallenges. This review initially explores the issue of domain specificity,\nwhere LLMs may struggle to provide precise answers to specialized questions\nwithin niche fields. The problem of knowledge forgetting arises as these LLMs\nmight find it hard to balance old and new information. The knowledge repetition\nphenomenon reveals that sometimes LLMs might deliver overly mechanized\nresponses, lacking depth and originality. Furthermore, knowledge illusion\ndescribes situations where LLMs might provide answers that seem insightful but\nare actually superficial, while knowledge toxicity focuses on harmful or biased\ninformation outputs. These challenges underscore problems in the training data\nand algorithmic design of LLMs. To address these issues, it's suggested to\ndiversify training data, fine-tune models, enhance transparency and\ninterpretability, and incorporate ethics and fairness training. Future\ntechnological trends might lean towards iterative methodologies, multimodal\nlearning, model personalization and customization, and real-time learning and\nfeedback mechanisms. In conclusion, future LLMs should prioritize fairness,\ntransparency, and ethics, ensuring they uphold high moral and ethical standards\nwhen serving humanity.",
        "authors": "Xiaoliang Chen, Liangbin Li, Le Chang, Yunhe Huang, Yuxuan Zhao, Yuxiao Zhang, Dinuo Li",
        "published": "2023-10-20",
        "updated": "2023-10-20",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2308.05345v3",
        "title": "RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language Model",
        "abstract": "Inspired by the recent success of large language models (LLMs) like ChatGPT,\nresearchers start to explore the adoption of LLMs for agile hardware design,\nsuch as generating design RTL based on natural-language instructions. However,\nin existing works, their target designs are all relatively simple and in a\nsmall scale, and proposed by the authors themselves, making a fair comparison\namong different LLM solutions challenging. In addition, many prior works only\nfocus on the design correctness, without evaluating the design qualities of\ngenerated design RTL. In this work, we propose an open-source benchmark named\nRTLLM, for generating design RTL with natural language instructions. To\nsystematically evaluate the auto-generated design RTL, we summarized three\nprogressive goals, named syntax goal, functionality goal, and design quality\ngoal. This benchmark can automatically provide a quantitative evaluation of any\ngiven LLM-based solution. Furthermore, we propose an easy-to-use yet\nsurprisingly effective prompt engineering technique named self-planning, which\nproves to significantly boost the performance of GPT-3.5 in our proposed\nbenchmark.",
        "authors": "Yao Lu, Shang Liu, Qijun Zhang, Zhiyao Xie",
        "published": "2023-08-10",
        "updated": "2023-11-11",
        "primary_cat": "cs.LG",
        "cats": [
            "cs.LG",
            "cs.AR"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2403.17553v1",
        "title": "RuBia: A Russian Language Bias Detection Dataset",
        "abstract": "Warning: this work contains upsetting or disturbing content.\n  Large language models (LLMs) tend to learn the social and cultural biases\npresent in the raw pre-training data. To test if an LLM's behavior is fair,\nfunctional datasets are employed, and due to their purpose, these datasets are\nhighly language and culture-specific. In this paper, we address a gap in the\nscope of multilingual bias evaluation by presenting a bias detection dataset\nspecifically designed for the Russian language, dubbed as RuBia. The RuBia\ndataset is divided into 4 domains: gender, nationality, socio-economic status,\nand diverse, each of the domains is further divided into multiple fine-grained\nsubdomains. Every example in the dataset consists of two sentences with the\nfirst reinforcing a potentially harmful stereotype or trope and the second\ncontradicting it. These sentence pairs were first written by volunteers and\nthen validated by native-speaking crowdsourcing workers. Overall, there are\nnearly 2,000 unique sentence pairs spread over 19 subdomains in RuBia. To\nillustrate the dataset's purpose, we conduct a diagnostic evaluation of\nstate-of-the-art or near-state-of-the-art LLMs and discuss the LLMs'\npredisposition to social biases.",
        "authors": "Veronika Grigoreva, Anastasiia Ivanova, Ilseyar Alimova, Ekaterina Artemova",
        "published": "2024-03-26",
        "updated": "2024-03-26",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2310.09219v5",
        "title": "\"Kelly is a Warm Person, Joseph is a Role Model\": Gender Biases in LLM-Generated Reference Letters",
        "abstract": "Large Language Models (LLMs) have recently emerged as an effective tool to\nassist individuals in writing various types of content, including professional\ndocuments such as recommendation letters. Though bringing convenience, this\napplication also introduces unprecedented fairness concerns. Model-generated\nreference letters might be directly used by users in professional scenarios. If\nunderlying biases exist in these model-constructed letters, using them without\nscrutinization could lead to direct societal harms, such as sabotaging\napplication success rates for female applicants. In light of this pressing\nissue, it is imminent and necessary to comprehensively study fairness issues\nand associated harms in this real-world use case. In this paper, we critically\nexamine gender biases in LLM-generated reference letters. Drawing inspiration\nfrom social science findings, we design evaluation methods to manifest biases\nthrough 2 dimensions: (1) biases in language style and (2) biases in lexical\ncontent. We further investigate the extent of bias propagation by analyzing the\nhallucination bias of models, a term that we define to be bias exacerbation in\nmodel-hallucinated contents. Through benchmarking evaluation on 2 popular LLMs-\nChatGPT and Alpaca, we reveal significant gender biases in LLM-generated\nrecommendation letters. Our findings not only warn against using LLMs for this\napplication without scrutinization, but also illuminate the importance of\nthoroughly studying hidden biases and harms in LLM-generated professional\ndocuments.",
        "authors": "Yixin Wan, George Pu, Jiao Sun, Aparna Garimella, Kai-Wei Chang, Nanyun Peng",
        "published": "2023-10-13",
        "updated": "2023-12-01",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2312.07420v1",
        "title": "FairSISA: Ensemble Post-Processing to Improve Fairness of Unlearning in LLMs",
        "abstract": "Training large language models (LLMs) is a costly endeavour in terms of time\nand computational resources. The large amount of training data used during the\nunsupervised pre-training phase makes it difficult to verify all data and,\nunfortunately, undesirable data may be ingested during training. Re-training\nfrom scratch is impractical and has led to the creation of the 'unlearning'\ndiscipline where models are modified to \"unlearn\" undesirable information\nwithout retraining. However, any modification can alter the behaviour of LLMs,\nespecially on key dimensions such as fairness. This is the first work that\nexamines this interplay between unlearning and fairness for LLMs. In\nparticular, we focus on a popular unlearning framework known as SISA [Bourtoule\net al., 2021], which creates an ensemble of models trained on disjoint shards.\nWe evaluate the performance-fairness trade-off for SISA, and empirically\ndemsontrate that SISA can indeed reduce fairness in LLMs. To remedy this, we\npropose post-processing bias mitigation techniques for ensemble models produced\nby SISA. We adapt the post-processing fairness improvement technique from\n[Hardt et al., 2016] to design three methods that can handle model ensembles,\nand prove that one of the methods is an optimal fair predictor for ensemble of\nmodels. Through experimental results, we demonstrate the efficacy of our\npost-processing framework called 'FairSISA'.",
        "authors": "Swanand Ravindra Kadhe, Anisa Halimi, Ambrish Rawat, Nathalie Baracaldo",
        "published": "2023-12-12",
        "updated": "2023-12-12",
        "primary_cat": "cs.LG",
        "cats": [
            "cs.LG",
            "cs.CY"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2404.03192v1",
        "title": "Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankers",
        "abstract": "The integration of Large Language Models (LLMs) in information retrieval has\nraised a critical reevaluation of fairness in the text-ranking models. LLMs,\nsuch as GPT models and Llama2, have shown effectiveness in natural language\nunderstanding tasks, and prior works (e.g., RankGPT) have also demonstrated\nthat the LLMs exhibit better performance than the traditional ranking models in\nthe ranking task. However, their fairness remains largely unexplored. This\npaper presents an empirical study evaluating these LLMs using the TREC Fair\nRanking dataset, focusing on the representation of binary protected attributes\nsuch as gender and geographic location, which are historically underrepresented\nin search outcomes. Our analysis delves into how these LLMs handle queries and\ndocuments related to these attributes, aiming to uncover biases in their\nranking algorithms. We assess fairness from both user and content perspectives,\ncontributing an empirical benchmark for evaluating LLMs as the fair ranker.",
        "authors": "Yuan Wang, Xuyang Wu, Hsin-Tai Wu, Zhiqiang Tao, Yi Fang",
        "published": "2024-04-04",
        "updated": "2024-04-04",
        "primary_cat": "cs.IR",
        "cats": [
            "cs.IR",
            "cs.CL"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2402.02680v1",
        "title": "Large Language Models are Geographically Biased",
        "abstract": "Large Language Models (LLMs) inherently carry the biases contained in their\ntraining corpora, which can lead to the perpetuation of societal harm. As the\nimpact of these foundation models grows, understanding and evaluating their\nbiases becomes crucial to achieving fairness and accuracy. We propose to study\nwhat LLMs know about the world we live in through the lens of geography. This\napproach is particularly powerful as there is ground truth for the numerous\naspects of human life that are meaningfully projected onto geographic space\nsuch as culture, race, language, politics, and religion. We show various\nproblematic geographic biases, which we define as systemic errors in geospatial\npredictions. Initially, we demonstrate that LLMs are capable of making accurate\nzero-shot geospatial predictions in the form of ratings that show strong\nmonotonic correlation with ground truth (Spearman's $\\rho$ of up to 0.89). We\nthen show that LLMs exhibit common biases across a range of objective and\nsubjective topics. In particular, LLMs are clearly biased against locations\nwith lower socioeconomic conditions (e.g. most of Africa) on a variety of\nsensitive subjective topics such as attractiveness, morality, and intelligence\n(Spearman's $\\rho$ of up to 0.70). Finally, we introduce a bias score to\nquantify this and find that there is significant variation in the magnitude of\nbias across existing LLMs.",
        "authors": "Rohin Manvi, Samar Khanna, Marshall Burke, David Lobell, Stefano Ermon",
        "published": "2024-02-05",
        "updated": "2024-02-05",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI",
            "cs.CY",
            "cs.LG"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2402.17916v2",
        "title": "LLM-Resistant Math Word Problem Generation via Adversarial Attacks",
        "abstract": "Large language models (LLMs) have significantly transformed the educational\nlandscape. As current plagiarism detection tools struggle to keep pace with\nLLMs' rapid advancements, the educational community faces the challenge of\nassessing students' true problem-solving abilities in the presence of LLMs. In\nthis work, we explore a new paradigm for ensuring fair evaluation -- generating\nadversarial examples which preserve the structure and difficulty of the\noriginal questions aimed for assessment, but are unsolvable by LLMs. Focusing\non the domain of math word problems, we leverage abstract syntax trees to\nstructurally generate adversarial examples that cause LLMs to produce incorrect\nanswers by simply editing the numeric values in the problems. We conduct\nexperiments on various open- and closed-source LLMs, quantitatively and\nqualitatively demonstrating that our method significantly degrades their math\nproblem-solving ability. We identify shared vulnerabilities among LLMs and\npropose a cost-effective approach to attack high-cost models. Additionally, we\nconduct automatic analysis on math problems and investigate the cause of\nfailure, offering a nuanced view into model's limitation.",
        "authors": "Roy Xie, Chengxuan Huang, Junlin Wang, Bhuwan Dhingra",
        "published": "2024-02-27",
        "updated": "2024-03-30",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2402.11764v1",
        "title": "ChatGPT Based Data Augmentation for Improved Parameter-Efficient Debiasing of LLMs",
        "abstract": "Large Language models (LLMs), while powerful, exhibit harmful social biases.\nDebiasing is often challenging due to computational costs, data constraints,\nand potential degradation of multi-task language capabilities. This work\nintroduces a novel approach utilizing ChatGPT to generate synthetic training\ndata, aiming to enhance the debiasing of LLMs. We propose two strategies:\nTargeted Prompting, which provides effective debiasing for known biases but\nnecessitates prior specification of bias in question; and General Prompting,\nwhich, while slightly less effective, offers debiasing across various\ncategories. We leverage resource-efficient LLM debiasing using adapter tuning\nand compare the effectiveness of our synthetic data to existing debiasing\ndatasets. Our results reveal that: (1) ChatGPT can efficiently produce\nhigh-quality training data for debiasing other LLMs; (2) data produced via our\napproach surpasses existing datasets in debiasing performance while also\npreserving internal knowledge of a pre-trained LLM; and (3) synthetic data\nexhibits generalizability across categories, effectively mitigating various\nbiases, including intersectional ones. These findings underscore the potential\nof synthetic data in advancing the fairness of LLMs with minimal retraining\ncost.",
        "authors": "Pengrui Han, Rafal Kocielnik, Adhithya Saravanan, Roy Jiang, Or Sharir, Anima Anandkumar",
        "published": "2024-02-19",
        "updated": "2024-02-19",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI",
            "cs.CY",
            "68T50",
            "I.2.7; K.4.1"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2305.13862v2",
        "title": "A Trip Towards Fairness: Bias and De-Biasing in Large Language Models",
        "abstract": "Cheap-to-Build Very Large-Language Models (CtB-LLMs) with affordable training\nare emerging as the next big revolution in natural language processing and\nunderstanding. These CtB-LLMs are democratizing access to trainable Very\nLarge-Language Models (VLLMs) and, thus, may represent the building blocks of\nmany NLP systems solving downstream tasks. Hence, a little or a large bias in\nCtB-LLMs may cause huge harm. In this paper, we performed a large investigation\nof the bias of three families of CtB-LLMs, and we showed that debiasing\ntechniques are effective and usable. Indeed, according to current tests, the\nLLaMA and the OPT families have an important bias in gender, race, religion,\nand profession. In contrast to the analysis for other LLMs, we discovered that\nbias depends not on the number of parameters but on the perplexity. Finally,\nthe debiasing of OPT using LoRA reduces bias up to 4.12 points in the\nnormalized stereotype score.",
        "authors": "Leonardo Ranaldi, Elena Sofia Ruzzetti, Davide Venditti, Dario Onorati, Fabio Massimo Zanzotto",
        "published": "2023-05-23",
        "updated": "2023-08-29",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2405.02219v1",
        "title": "FairEvalLLM. A Comprehensive Framework for Benchmarking Fairness in Large Language Model Recommender Systems",
        "abstract": "This paper presents a framework for evaluating fairness in recommender\nsystems powered by Large Language Models (RecLLMs), addressing the need for a\nunified approach that spans various fairness dimensions including sensitivity\nto user attributes, intrinsic fairness, and discussions of fairness based on\nunderlying benefits. In addition, our framework introduces counterfactual\nevaluations and integrates diverse user group considerations to enhance the\ndiscourse on fairness evaluation for RecLLMs.\n  Our key contributions include the development of a robust framework for\nfairness evaluation in LLM-based recommendations and a structured method to\ncreate \\textit{informative user profiles} from demographic data, historical\nuser preferences, and recent interactions. We argue that the latter is\nessential for enhancing personalization in such systems, especially in\ntemporal-driven scenarios. We demonstrate the utility of our framework through\npractical applications on two datasets, LastFM-1K and ML-1M. We conduct\nexperiments on a subsample of 80 users from each dataset, testing and assessing\nthe effectiveness of various prompt construction scenarios and in-context\nlearning, comprising more than 50 scenarios. This results in more than 4000\nrecommendations (80 * 50 = 4000). Our study reveals that while there are no\nsignificant unfairness issues in scenarios involving sensitive attributes, some\nconcerns remain. However, in terms of intrinsic fairness, which does not\ninvolve direct sensitivity, unfairness across demographic groups remains\nsignificant. The code and data used for this paper are available at:\n\\url{https://shorturl.at/awBFM}.",
        "authors": "Yashar Deldjoo",
        "published": "2024-05-03",
        "updated": "2024-05-03",
        "primary_cat": "cs.IR",
        "cats": [
            "cs.IR"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2404.18276v1",
        "title": "Bias Neutralization Framework: Measuring Fairness in Large Language Models with Bias Intelligence Quotient (BiQ)",
        "abstract": "The burgeoning influence of Large Language Models (LLMs) in shaping public\ndiscourse and decision-making underscores the imperative to address inherent\nbiases within these AI systems. In the wake of AI's expansive integration\nacross sectors, addressing racial bias in LLMs has never been more critical.\nThis paper introduces a novel framework called Comprehensive Bias\nNeutralization Framework (CBNF) which embodies an innovative approach to\nquantifying and mitigating biases within LLMs. Our framework combines the Large\nLanguage Model Bias Index (LLMBI) [Oketunji, A., Anas, M., Saina, D., (2023)]\nand Bias removaL with No Demographics (BLIND) [Orgad, H., Belinkov, Y. (2023)]\nmethodologies to create a new metric called Bias Intelligence Quotient\n(BiQ)which detects, measures, and mitigates racial bias in LLMs without\nreliance on demographic annotations.\n  By introducing a new metric called BiQ that enhances LLMBI with additional\nfairness metrics, CBNF offers a multi-dimensional metric for bias assessment,\nunderscoring the necessity of a nuanced approach to fairness in AI [Mehrabi et\nal., 2021]. This paper presents a detailed analysis of Latimer AI (a language\nmodel incrementally trained on black history and culture) in comparison to\nChatGPT 3.5, illustrating Latimer AI's efficacy in detecting racial, cultural,\nand gender biases through targeted training and refined bias mitigation\nstrategies [Latimer & Bender, 2023].",
        "authors": "Malur Narayan, John Pasmore, Elton Sampaio, Vijay Raghavan, Gabriella Waters",
        "published": "2024-04-28",
        "updated": "2024-04-28",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI",
            "D.1; I.2"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2308.05374v2",
        "title": "Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment",
        "abstract": "Ensuring alignment, which refers to making models behave in accordance with\nhuman intentions [1,2], has become a critical task before deploying large\nlanguage models (LLMs) in real-world applications. For instance, OpenAI devoted\nsix months to iteratively aligning GPT-4 before its release [3]. However, a\nmajor challenge faced by practitioners is the lack of clear guidance on\nevaluating whether LLM outputs align with social norms, values, and\nregulations. This obstacle hinders systematic iteration and deployment of LLMs.\nTo address this issue, this paper presents a comprehensive survey of key\ndimensions that are crucial to consider when assessing LLM trustworthiness. The\nsurvey covers seven major categories of LLM trustworthiness: reliability,\nsafety, fairness, resistance to misuse, explainability and reasoning, adherence\nto social norms, and robustness. Each major category is further divided into\nseveral sub-categories, resulting in a total of 29 sub-categories.\nAdditionally, a subset of 8 sub-categories is selected for further\ninvestigation, where corresponding measurement studies are designed and\nconducted on several widely-used LLMs. The measurement results indicate that,\nin general, more aligned models tend to perform better in terms of overall\ntrustworthiness. However, the effectiveness of alignment varies across the\ndifferent trustworthiness categories considered. This highlights the importance\nof conducting more fine-grained analyses, testing, and making continuous\nimprovements on LLM alignment. By shedding light on these key dimensions of LLM\ntrustworthiness, this paper aims to provide valuable insights and guidance to\npractitioners in the field. Understanding and addressing these concerns will be\ncrucial in achieving reliable and ethically sound deployment of LLMs in various\napplications.",
        "authors": "Yang Liu, Yuanshun Yao, Jean-Francois Ton, Xiaoying Zhang, Ruocheng Guo, Hao Cheng, Yegor Klochkov, Muhammad Faaiz Taufiq, Hang Li",
        "published": "2023-08-10",
        "updated": "2024-03-21",
        "primary_cat": "cs.AI",
        "cats": [
            "cs.AI",
            "cs.LG"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2303.01248v3",
        "title": "Can ChatGPT Assess Human Personalities? A General Evaluation Framework",
        "abstract": "Large Language Models (LLMs) especially ChatGPT have produced impressive\nresults in various areas, but their potential human-like psychology is still\nlargely unexplored. Existing works study the virtual personalities of LLMs but\nrarely explore the possibility of analyzing human personalities via LLMs. This\npaper presents a generic evaluation framework for LLMs to assess human\npersonalities based on Myers Briggs Type Indicator (MBTI) tests. Specifically,\nwe first devise unbiased prompts by randomly permuting options in MBTI\nquestions and adopt the average testing result to encourage more impartial\nanswer generation. Then, we propose to replace the subject in question\nstatements to enable flexible queries and assessments on different subjects\nfrom LLMs. Finally, we re-formulate the question instructions in a manner of\ncorrectness evaluation to facilitate LLMs to generate clearer responses. The\nproposed framework enables LLMs to flexibly assess personalities of different\ngroups of people. We further propose three evaluation metrics to measure the\nconsistency, robustness, and fairness of assessment results from\nstate-of-the-art LLMs including ChatGPT and GPT-4. Our experiments reveal\nChatGPT's ability to assess human personalities, and the average results\ndemonstrate that it can achieve more consistent and fairer assessments in spite\nof lower robustness against prompt biases compared with InstructGPT.",
        "authors": "Haocong Rao, Cyril Leung, Chunyan Miao",
        "published": "2023-03-01",
        "updated": "2023-10-13",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2308.10149v2",
        "title": "A Survey on Fairness in Large Language Models",
        "abstract": "Large Language Models (LLMs) have shown powerful performance and development\nprospects and are widely deployed in the real world. However, LLMs can capture\nsocial biases from unprocessed training data and propagate the biases to\ndownstream tasks. Unfair LLM systems have undesirable social impacts and\npotential harms. In this paper, we provide a comprehensive review of related\nresearch on fairness in LLMs. Considering the influence of parameter magnitude\nand training paradigm on research strategy, we divide existing fairness\nresearch into oriented to medium-sized LLMs under pre-training and fine-tuning\nparadigms and oriented to large-sized LLMs under prompting paradigms. First,\nfor medium-sized LLMs, we introduce evaluation metrics and debiasing methods\nfrom the perspectives of intrinsic bias and extrinsic bias, respectively. Then,\nfor large-sized LLMs, we introduce recent fairness research, including fairness\nevaluation, reasons for bias, and debiasing methods. Finally, we discuss and\nprovide insight on the challenges and future directions for the development of\nfairness in LLMs.",
        "authors": "Yingji Li, Mengnan Du, Rui Song, Xin Wang, Ying Wang",
        "published": "2023-08-20",
        "updated": "2024-02-21",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2305.07609v3",
        "title": "Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation",
        "abstract": "The remarkable achievements of Large Language Models (LLMs) have led to the\nemergence of a novel recommendation paradigm -- Recommendation via LLM\n(RecLLM). Nevertheless, it is important to note that LLMs may contain social\nprejudices, and therefore, the fairness of recommendations made by RecLLM\nrequires further investigation. To avoid the potential risks of RecLLM, it is\nimperative to evaluate the fairness of RecLLM with respect to various sensitive\nattributes on the user side. Due to the differences between the RecLLM paradigm\nand the traditional recommendation paradigm, it is problematic to directly use\nthe fairness benchmark of traditional recommendation. To address the dilemma,\nwe propose a novel benchmark called Fairness of Recommendation via LLM\n(FaiRLLM). This benchmark comprises carefully crafted metrics and a dataset\nthat accounts for eight sensitive attributes1 in two recommendation scenarios:\nmusic and movies. By utilizing our FaiRLLM benchmark, we conducted an\nevaluation of ChatGPT and discovered that it still exhibits unfairness to some\nsensitive attributes when generating recommendations. Our code and dataset can\nbe found at https://github.com/jizhi-zhang/FaiRLLM.",
        "authors": "Jizhi Zhang, Keqin Bao, Yang Zhang, Wenjie Wang, Fuli Feng, Xiangnan He",
        "published": "2023-05-12",
        "updated": "2023-10-17",
        "primary_cat": "cs.IR",
        "cats": [
            "cs.IR",
            "cs.CL",
            "cs.CY"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2311.18140v1",
        "title": "ROBBIE: Robust Bias Evaluation of Large Generative Language Models",
        "abstract": "As generative large language models (LLMs) grow more performant and\nprevalent, we must develop comprehensive enough tools to measure and improve\ntheir fairness. Different prompt-based datasets can be used to measure social\nbias across multiple text domains and demographic axes, meaning that testing\nLLMs on more datasets can potentially help us characterize their biases more\nfully, and better ensure equal and equitable treatment of marginalized\ndemographic groups. In this work, our focus is two-fold:\n  (1) Benchmarking: a comparison of 6 different prompt-based bias and toxicity\nmetrics across 12 demographic axes and 5 families of generative LLMs. Out of\nthose 6 metrics, AdvPromptSet and HolisticBiasR are novel datasets proposed in\nthe paper. The comparison of those benchmarks gives us insights about the bias\nand toxicity of the compared models. Therefore, we explore the frequency of\ndemographic terms in common LLM pre-training corpora and how this may relate to\nmodel biases.\n  (2) Mitigation: we conduct a comprehensive study of how well 3 bias/toxicity\nmitigation techniques perform across our suite of measurements. ROBBIE aims to\nprovide insights for practitioners while deploying a model, emphasizing the\nneed to not only measure potential harms, but also understand how they arise by\ncharacterizing the data, mitigate harms once found, and balance any trade-offs.\nWe open-source our analysis code in hopes of encouraging broader measurements\nof bias in future LLMs.",
        "authors": "David Esiobu, Xiaoqing Tan, Saghar Hosseini, Megan Ung, Yuchen Zhang, Jude Fernandes, Jane Dwivedi-Yu, Eleonora Presani, Adina Williams, Eric Michael Smith",
        "published": "2023-11-29",
        "updated": "2023-11-29",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2310.18333v3",
        "title": "She had Cobalt Blue Eyes: Prompt Testing to Create Aligned and Sustainable Language Models",
        "abstract": "As the use of large language models (LLMs) increases within society, as does\nthe risk of their misuse. Appropriate safeguards must be in place to ensure LLM\noutputs uphold the ethical standards of society, highlighting the positive role\nthat artificial intelligence technologies can have. Recent events indicate\nethical concerns around conventionally trained LLMs, leading to overall unsafe\nuser experiences. This motivates our research question: how do we ensure LLM\nalignment? In this work, we introduce a test suite of unique prompts to foster\nthe development of aligned LLMs that are fair, safe, and robust. We show that\nprompting LLMs at every step of the development pipeline, including data\ncuration, pre-training, and fine-tuning, will result in an overall more\nresponsible model. Our test suite evaluates outputs from four state-of-the-art\nlanguage models: GPT-3.5, GPT-4, OPT, and LLaMA-2. The assessment presented in\nthis paper highlights a gap between societal alignment and the capabilities of\ncurrent LLMs. Additionally, implementing a test suite such as ours lowers the\nenvironmental overhead of making models safe and fair.",
        "authors": "Veronica Chatrath, Oluwanifemi Bamgbose, Shaina Raza",
        "published": "2023-10-20",
        "updated": "2023-12-15",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2402.12150v1",
        "title": "Your Large Language Model is Secretly a Fairness Proponent and You Should Prompt it Like One",
        "abstract": "The widespread adoption of large language models (LLMs) underscores the\nurgent need to ensure their fairness. However, LLMs frequently present dominant\nviewpoints while ignoring alternative perspectives from minority parties,\nresulting in potential biases. We hypothesize that these fairness-violating\nbehaviors occur because LLMs express their viewpoints using a human personality\nthat represents the majority of training data. In response to this, we validate\nthat prompting LLMs with specific roles can allow LLMs to express diverse\nviewpoints. Building on this insight and observation, we develop FairThinking,\na pipeline designed to automatically generate roles that enable LLMs to\narticulate diverse perspectives for fair expressions. To evaluate FairThinking,\nwe create a dataset with a thousand items covering three fairness-related\ntopics and conduct experiments on GPT-3.5, GPT-4, Llama2, and Mistral to\ndemonstrate its superior performance.",
        "authors": "Tianlin Li, Xiaoyu Zhang, Chao Du, Tianyu Pang, Qian Liu, Qing Guo, Chao Shen, Yang Liu",
        "published": "2024-02-19",
        "updated": "2024-02-19",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI",
            "I.2; J.4"
        ],
        "category": "LLM Fairness"
    },
    {
        "url": "http://arxiv.org/abs/2401.01262v2",
        "title": "Fairness Certification for Natural Language Processing and Large Language Models",
        "abstract": "Natural Language Processing (NLP) plays an important role in our daily lives,\nparticularly due to the enormous progress of Large Language Models (LLM).\nHowever, NLP has many fairness-critical use cases, e.g., as an expert system in\nrecruitment or as an LLM-based tutor in education. Since NLP is based on human\nlanguage, potentially harmful biases can diffuse into NLP systems and produce\nunfair results, discriminate against minorities or generate legal issues.\nHence, it is important to develop a fairness certification for NLP approaches.\nWe follow a qualitative research approach towards a fairness certification for\nNLP. In particular, we have reviewed a large body of literature on algorithmic\nfairness, and we have conducted semi-structured expert interviews with a wide\nrange of experts from that area. We have systematically devised six fairness\ncriteria for NLP, which can be further refined into 18 sub-categories. Our\ncriteria offer a foundation for operationalizing and testing processes to\ncertify fairness, both from the perspective of the auditor and the audited\norganization.",
        "authors": "Vincent Freiberger, Erik Buchmann",
        "published": "2024-01-02",
        "updated": "2024-01-03",
        "primary_cat": "cs.CL",
        "cats": [
            "cs.CL",
            "cs.AI",
            "cs.CY",
            "cs.LG",
            "68T50",
            "I.2.7"
        ],
        "category": "LLM Fairness"
    }
]